In this script we conduct the estimation for the measure_arguments approach.

PROGRAMS=pg_arguments_full5_c200_opc15x2 SAMPLESIZE=50 NSAMPLES=1`.

Expected a result file revm_pg_arguments_full5_c200_opc15x2_.csv.

# the programs file is too large to be placed in github
programs = read.csv(paste("../../local/", program_set_codename, ".csv", sep=""))

results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]

all_envs = c(env)
measurements = sqldf("SELECT opcode, op_count, arg0, arg1, arg2, sample_id, run_id, measure_total_time_ns, env, results.program_id
                     FROM results
                     INNER JOIN
                       programs ON(results.program_id = programs.program_id)
                     ")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)
##   opcode op_count arg0 arg1 arg2 sample_id run_id measure_total_time_ns  env
## 1    ADD        0   25   27   NA         0      1                   579 revm
## 2    ADD       15   25   27   NA         0      1                   628 revm
## 3    ADD       30   25   27   NA         0      1                   696 revm
## 4    ADD        0   14    9   NA         0      1                   576 revm
## 5    ADD       15   14    9   NA         0      1                   625 revm
## 6    ADD       30   14    9   NA         0      1                   662 revm
##   program_id
## 1      ADD_0
## 2      ADD_1
## 3      ADD_2
## 4      ADD_3
## 5      ADD_4
## 6      ADD_5

Remove outliers if needed.

# Extracts all OPCODEs from the `programs` data frame of the given arity (args taken off the stack).
extract_opcodes <- function(arity) {
  if (!missing(arity)) {
    if (arity == 0) {
      programs = programs[which(is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
    }
    if (arity == 1) {
      programs = programs[which(!is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
    }
    if (arity == 2) {
      programs = programs[which(!is.na(programs$arg1) & is.na(programs$arg2)), ]
    }
    if (arity == 3) {
      programs = programs[which(!is.na(programs$arg2)), ]
    }
  }
  unique(programs$opcode)
}
if ( (!removed_outliers) && (!removed_outliers_2)) {
  boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
}
if (removed_outliers) {
  par(mfrow=c(length(all_envs)*2, 1))
  
  # before
  boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))

  measurements = remove_outliers(measurements, 'measure_total_time_ns', FALSE)
  
  # after
  boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'no_outliers'))
}
all_opcodes = extract_opcodes()
nullary_opcodes = extract_opcodes(0)
unary_opcodes = extract_opcodes(1)
binary_opcodes = extract_opcodes(2)
ternary_opcodes = extract_opcodes(3)

div_opcodes = c('DIV', 'MOD', 'SDIV', 'SMOD')
measurements$expensive = NA
measurements[which(measurements$opcode %in% div_opcodes), ]$expensive =
  measurements[which(measurements$opcode %in% div_opcodes), ]$arg0 >
  measurements[which(measurements$opcode %in% div_opcodes), ]$arg1
# remember that argX is the byte-size of the argument in these measurements
measurements[which(measurements$opcode == 'ADDMOD'), ]$expensive =
  8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg0 +
  8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg1 > 
  8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg2
measurements[which(measurements$opcode == 'MULMOD'), ]$expensive =
  measurements[which(measurements$opcode == 'MULMOD'), ]$arg0 +
  measurements[which(measurements$opcode == 'MULMOD'), ]$arg1 >
  measurements[which(measurements$opcode == 'MULMOD'), ]$arg2
if (removed_outliers_2) {
  measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}

Detailed view

This is massive and detailed overview on the impact of arguments. Because of the number of charts, only op count = 30 is eligible. Feel free to change it, but that should not be anyhow more informative. The visualizations do not guarantee that all dependencies are clearly seen. Especially for binary and ternary opcodes where impacts of arg0, arg1 and arg2 are mixed. But if a dependency is graphically noticeable that you should expect also statistical dependency.

for (env in all_envs) {
  for (opcode in unary_opcodes) {
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg0', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg0', 'opcount 15'))
    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg0', 'opcount 30'))
  } 
  for (opcode in binary_opcodes) {
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg0', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg0', 'opcount 15'))
    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg0', 'opcount 30'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg1', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg1', 'opcount 15'))
    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg1', 'opcount 30'))
  } 
  for (opcode in ternary_opcodes) {
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg0', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg0', 'opcount 15'))
    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg0', 'opcount 30'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg1', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg1', 'opcount 15'))
    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg1', 'opcount 30'))
#    plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg2', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg2', 'opcount 15'))
    plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg2', 'opcount 30'))
  } 
}

Models

Notes: 1. Outliers need to be removed if detected 2. The argX:op_count interactions measure the impact on the OPCODE 3. The argX are just auxiliary variables added to exclude the effect of cheaper/more expensive PUSHes. We only want to extract the effect of the argument on the measured OPCODE repeated op_count times.

# Every `arg` coefficient represents the impact of the argument's byte size growing by 1.
# We treat as impactful the arguments where p-value is effectively zero. The previous approach was:
# Treat as impactful the arguments, where:
# 1. The estimate is significant with confidence 0.001
# 2. The increase of arg's byte size by 1 will increase the cost by more than 1%
# but it turned out to be much less stable in practice.
p_value_thresh = 1e-30
# p_value_thresh = 0.001
impact_ratio = 0.00
# impact_ratio = 0.01

arg_lm <- function(df, opcode, env, formula) {
  data = df[which(df$opcode==opcode & df$env==env), ]
  lm(formula, data=data)
}

# Adds the results from the estimated `model` to the `results_df` data frame.
# You need to provide the corresponding `opcode`, `env` and `arity`.
# `results_df` is assumed to have the columns as the `first_pass` data frame has (see below)
add_arg_results <- function(model, opcode, env, results_df, arity) {
  stopifnot(arity > 0)

  all_coefficients = summary(model)$coefficients
  arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
  pure_op_count_coeff = all_coefficients["op_count", 1]
  # will be filled if any is impacting
  args_ns = c(NA, NA, NA)
  # will be always if arg present
  args_ns_raw = c(NA, NA, NA)
  args_ns_p = c(NA, NA, NA)

  if (arity == 1) {
    # there's only one arg coefficient here, silly R forces us to take a special case path...
    has_significant = arg_coefficients[4] < p_value_thresh
  
    if (has_significant) {
      coefficient_impact = abs(arg_coefficients[1])
      has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
    } else {
      has_impacting = FALSE
    }
    if (has_impacting) {
      args_ns[1] = arg_coefficients[1]
    }
    args_ns_raw[1] = arg_coefficients[1]
    args_ns_p[1] = arg_coefficients[4]
  } else {
    significant = arg_coefficients[, 4] < p_value_thresh
    has_significant = length(which(significant)) > 0
  
    coefficient_impact = abs(arg_coefficients[, 1])
    can_impact = significant & coefficient_impact > pure_op_count_coeff * impact_ratio
    has_impacting = length(which(can_impact)) > 0
    args_ns[which(can_impact)] = arg_coefficients[which(can_impact), 1]
    args_ns_raw[1:arity] = arg_coefficients[1:arity, 1]
    args_ns_p[1:arity] = arg_coefficients[1:arity, 4]
  }
  
  # NAs for the "expensive" arg columns. See above for the columns layout
  results_df[nrow(results_df) + 1, ] = c(opcode, env, has_significant, has_impacting, pure_op_count_coeff, args_ns, NA, args_ns_raw, NA, args_ns_p, NA)
  return(results_df)
}

# Adds the results from the estimated `model` to the `results_df` data frame, where the model is
# specifically the one gauged towards the "division" OPCODEs like `DIV`.
# See also `add_arg_results`
add_arg_expensive_results <- function(model, opcode, env, results_df, arity) {
  stopifnot(arity > 0)

  all_coefficients = summary(model)$coefficients
  pure_op_count_coeff = all_coefficients["op_count", 1]
  expensive = NA
  
  # there's only one arg coefficient here, silly R forces us to take a special case path...
  has_significant = all_coefficients['op_count:expensiveTRUE', 4] < p_value_thresh

  if (has_significant) {
    coefficient_impact = abs(all_coefficients['op_count:expensiveTRUE', 1])
    has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
  } else {
    has_impacting = FALSE
  }
  if (has_impacting) {
    expensive = all_coefficients['op_count:expensiveTRUE', 1]
  }
  expensive_raw = all_coefficients['op_count:expensiveTRUE', 1]
  expensive_p = all_coefficients['op_count:expensiveTRUE', 4]
  results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns'] = expensive
  results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_raw'] = expensive_raw
  results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_p'] = expensive_p
  return(results_df)
}

# Goes through all the families of OPCODEs and fits and displays their respective `measure_arguments`
# models.
# Results are gathered in a common `results_df` data frame.
analyze_for_env <- function(df, results_df, env) {
  for (opcode in unary_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg0:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_results(model, opcode, env, results_df, 1)
  }
  for (opcode in binary_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg0:op_count + arg1:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_results(model, opcode, env, results_df, 2)
  }
  for (opcode in ternary_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + arg0:op_count + arg1:op_count + arg2:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_results(model, opcode, env, results_df, 3)
  }
  for (opcode in div_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + expensive:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_expensive_results(model, opcode, env, results_df, 2)
  }
  for (opcode in c('ADDMOD', 'MULMOD')) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + expensive:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_expensive_results(model, opcode, env, results_df, 3)
  }
  return(results_df)
}

This is the so-called “first-pass” at the estimation procedure, where we estimated all possible argument impact variables for all OPCODEs. We gather all the results in the first_pass table, inspect this to see where the arguments turned out to be significantly impacting the computation cost.

first_pass = data.frame(matrix(ncol = 17, nrow = 0))
colnames(first_pass) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
                          'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns',
                          'arg0_ns_raw', 'arg1_ns_raw', 'arg2_ns_raw', 'expensive_ns_raw',
                          'arg0_ns_p', 'arg1_ns_p', 'arg2_ns_p',  'expensive_ns_p')

first_pass = analyze_for_env(measurements, first_pass, env)
## [1] "ISZERO" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.819 -3.962 -1.929  5.676 12.699 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   578.9756956   0.7355718 787.110 <0.0000000000000002 ***
## op_count        2.1569271   0.0379494  56.837 <0.0000000000000002 ***
## arg0           -0.0156510   0.0414011  -0.378               0.706    
## op_count:arg0   0.0009636   0.0021376   0.451               0.652    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.12 on 473 degrees of freedom
## Multiple R-squared:  0.9643, Adjusted R-squared:  0.9641 
## F-statistic:  4263 on 3 and 473 DF,  p-value: < 0.00000000000000022
## 
## [1] "NOT"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.090 -3.987 -2.054  3.968 15.911 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   578.2879039   0.7376285 783.983 <0.0000000000000002 ***
## op_count        2.1934399   0.0381841  57.444 <0.0000000000000002 ***
## arg0           -0.0111608   0.0381783  -0.292                0.77    
## op_count:arg0   0.0003694   0.0019569   0.189                0.85    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.788 on 477 degrees of freedom
## Multiple R-squared:  0.9692, Adjusted R-squared:  0.969 
## F-statistic:  5002 on 3 and 477 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATALOAD" "revm"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -70.21 -38.69 -19.15  37.73 155.71 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   839.35670324   6.41890920 130.763 <0.0000000000000002 ***
## op_count        2.83495687   0.33194607   8.540 <0.0000000000000002 ***
## arg0            0.00006530   0.00064051   0.102               0.919    
## op_count:arg0   0.00003009   0.00003312   0.908               0.364    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 47.68 on 590 degrees of freedom
## Multiple R-squared:  0.3911, Adjusted R-squared:  0.388 
## F-statistic: 126.3 on 3 and 590 DF,  p-value: < 0.00000000000000022
## 
## [1] "POP"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.4916  -4.2137  -0.2337   3.8883  17.0101 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   374.2432363   0.7547139 495.874 <0.0000000000000002 ***
## op_count        1.9920421   0.0390774  50.977 <0.0000000000000002 ***
## arg0           -0.0010542   0.0392292  -0.027               0.979    
## op_count:arg0  -0.0007419   0.0020428  -0.363               0.717    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.435 on 533 degrees of freedom
## Multiple R-squared:  0.9525, Adjusted R-squared:  0.9522 
## F-statistic:  3563 on 3 and 533 DF,  p-value: < 0.00000000000000022
## 
## [1] "MLOAD" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -83.22 -42.45 -17.03  38.48 161.42 
## 
## Coefficients:
##                    Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept)   855.356396346   7.259653543 117.823 <0.0000000000000002 ***
## op_count        3.475402738   0.373856370   9.296 <0.0000000000000002 ***
## arg0           -0.000477746   0.000708767  -0.674               0.501    
## op_count:arg0  -0.000002212   0.000036667  -0.060               0.952    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 51.97 on 581 degrees of freedom
## Multiple R-squared:  0.4039, Adjusted R-squared:  0.4008 
## F-statistic: 131.2 on 3 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMPI" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -27.25 -15.67 -10.87  22.73  47.11 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   557.6079528   2.7441217 203.201 <0.0000000000000002 ***
## op_count        3.9488737   0.1426012  27.692 <0.0000000000000002 ***
## arg0            0.0108371   0.1485570   0.073               0.942    
## op_count:arg0   0.0002968   0.0077169   0.038               0.969    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.24 on 524 degrees of freedom
## Multiple R-squared:  0.8504, Adjusted R-squared:  0.8495 
## F-statistic: 992.6 on 3 and 524 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP1" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.949 -4.828 -2.291  4.747 23.868 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   578.812699   0.947085 611.152 <0.0000000000000002 ***
## op_count        2.324822   0.048384  48.049 <0.0000000000000002 ***
## arg0           -0.016290   0.048576  -0.335               0.738    
## op_count:arg0   0.002327   0.002487   0.935               0.350    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.168 on 482 degrees of freedom
## Multiple R-squared:  0.957,  Adjusted R-squared:  0.9568 
## F-statistic:  3579 on 3 and 482 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP2" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.105 -4.068 -1.758  3.974 24.885 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   578.3109367   0.7274979 794.931 <0.0000000000000002 ***
## op_count        2.3274369   0.0374468  62.153 <0.0000000000000002 ***
## arg0           -0.0276276   0.0384189  -0.719               0.472    
## op_count:arg0   0.0008316   0.0019668   0.423               0.673    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.161 on 483 degrees of freedom
## Multiple R-squared:  0.969,  Adjusted R-squared:  0.9688 
## F-statistic:  5030 on 3 and 483 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP3" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.456 -3.522 -2.353  4.693 22.846 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   578.5685962   0.7516405 769.741 <0.0000000000000002 ***
## op_count        2.3260793   0.0394120  59.020 <0.0000000000000002 ***
## arg0           -0.0093672   0.0401027  -0.234               0.815    
## op_count:arg0  -0.0008342   0.0021215  -0.393               0.694    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.106 on 485 degrees of freedom
## Multiple R-squared:  0.9691, Adjusted R-squared:  0.9689 
## F-statistic:  5077 on 3 and 485 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP4" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.727 -4.054 -2.067  4.696 19.795 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   578.68066979   0.76783935 753.648 <0.0000000000000002 ***
## op_count        2.28679216   0.03972834  57.561 <0.0000000000000002 ***
## arg0            0.02335002   0.04042810   0.578               0.564    
## op_count:arg0   0.00009093   0.00211170   0.043               0.966    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.274 on 477 degrees of freedom
## Multiple R-squared:  0.966,  Adjusted R-squared:  0.9658 
## F-statistic:  4517 on 3 and 477 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP5" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.730 -4.077 -1.489  4.001 18.622 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   577.9527723   0.7323031 789.226 <0.0000000000000002 ***
## op_count        2.3098371   0.0365195  63.249 <0.0000000000000002 ***
## arg0            0.0118503   0.0372156   0.318               0.750    
## op_count:arg0   0.0002225   0.0018833   0.118               0.906    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.892 on 486 degrees of freedom
## Multiple R-squared:  0.9713, Adjusted R-squared:  0.9711 
## F-statistic:  5483 on 3 and 486 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP6" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.481 -3.555 -1.979  4.245 12.470 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   578.06324541   0.71939831 803.537 <0.0000000000000002 ***
## op_count        2.31675716   0.03733607  62.051 <0.0000000000000002 ***
## arg0           -0.00284664   0.03737133  -0.076               0.939    
## op_count:arg0  -0.00002326   0.00194023  -0.012               0.990    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.686 on 484 degrees of freedom
## Multiple R-squared:  0.9737, Adjusted R-squared:  0.9735 
## F-statistic:  5975 on 3 and 484 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP7" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.835 -3.934 -1.961  4.219 27.352 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   582.690493   0.885051 658.369 <0.0000000000000002 ***
## op_count        2.275245   0.045313  50.212 <0.0000000000000002 ***
## arg0            0.009016   0.047627   0.189               0.850    
## op_count:arg0  -0.001024   0.002415  -0.424               0.672    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.788 on 477 degrees of freedom
## Multiple R-squared:  0.9577, Adjusted R-squared:  0.9574 
## F-statistic:  3596 on 3 and 477 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP8" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.208 -4.345 -2.370  4.429 21.466 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   586.398909   0.738918 793.591 <0.0000000000000002 ***
## op_count        2.269390   0.038540  58.884 <0.0000000000000002 ***
## arg0            0.042591   0.042119   1.011               0.312    
## op_count:arg0  -0.001588   0.002206  -0.720               0.472    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.265 on 479 degrees of freedom
## Multiple R-squared:  0.965,  Adjusted R-squared:  0.9648 
## F-statistic:  4407 on 3 and 479 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP9" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.661 -3.482 -2.171  4.420 13.907 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   578.402120   0.676035 855.580 <0.0000000000000002 ***
## op_count        2.289866   0.035410  64.668 <0.0000000000000002 ***
## arg0            0.003972   0.036455   0.109               0.913    
## op_count:arg0  -0.001678   0.001904  -0.881               0.379    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.554 on 475 degrees of freedom
## Multiple R-squared:  0.9737, Adjusted R-squared:  0.9735 
## F-statistic:  5851 on 3 and 475 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP10" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.319 -4.365 -2.776  4.517 27.299 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   578.6341273   0.9514060 608.188 <0.0000000000000002 ***
## op_count        2.3133891   0.0482273  47.968 <0.0000000000000002 ***
## arg0            0.0235696   0.0480942   0.490               0.624    
## op_count:arg0  -0.0004083   0.0024323  -0.168               0.867    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.266 on 483 degrees of freedom
## Multiple R-squared:  0.953,  Adjusted R-squared:  0.9527 
## F-statistic:  3262 on 3 and 483 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP11" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.454 -3.859 -2.408  4.488 16.565 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   591.439060   0.781988 756.327 <0.0000000000000002 ***
## op_count        2.348242   0.040411  58.109 <0.0000000000000002 ***
## arg0            0.004248   0.041638   0.102               0.919    
## op_count:arg0  -0.001043   0.002114  -0.493               0.622    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.115 on 472 degrees of freedom
## Multiple R-squared:  0.9694, Adjusted R-squared:  0.9692 
## F-statistic:  4977 on 3 and 472 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP12" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.205 -3.822 -1.992  4.013 16.806 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   578.3169483   0.6845829 844.773 <0.0000000000000002 ***
## op_count        2.2909562   0.0351509  65.175 <0.0000000000000002 ***
## arg0           -0.0112078   0.0362752  -0.309               0.757    
## op_count:arg0  -0.0005016   0.0018653  -0.269               0.788    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.75 on 488 degrees of freedom
## Multiple R-squared:  0.9722, Adjusted R-squared:  0.9721 
## F-statistic:  5698 on 3 and 488 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP13" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.397 -4.549 -2.486  4.304 23.257 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   582.537259   0.963433 604.647 <0.0000000000000002 ***
## op_count        2.340797   0.050008  46.809 <0.0000000000000002 ***
## arg0           -0.005407   0.048592  -0.111               0.911    
## op_count:arg0   0.001144   0.002530   0.452               0.651    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.279 on 492 degrees of freedom
## Multiple R-squared:  0.9551, Adjusted R-squared:  0.9548 
## F-statistic:  3488 on 3 and 492 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP14" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.829 -4.218 -2.664  4.627 24.766 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   583.0766300   0.8712332 669.254 <0.0000000000000002 ***
## op_count        2.2686351   0.0456314  49.717 <0.0000000000000002 ***
## arg0            0.0235125   0.0479081   0.491               0.624    
## op_count:arg0  -0.0007934   0.0024936  -0.318               0.750    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.015 on 479 degrees of freedom
## Multiple R-squared:  0.9543, Adjusted R-squared:  0.954 
## F-statistic:  3335 on 3 and 479 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP15" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.325 -4.859 -1.947  3.705 26.104 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   578.582807   1.062377 544.612 <0.0000000000000002 ***
## op_count        2.364935   0.053834  43.930 <0.0000000000000002 ***
## arg0            0.012556   0.055506   0.226               0.821    
## op_count:arg0  -0.001400   0.002812  -0.498               0.619    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.546 on 489 degrees of freedom
## Multiple R-squared:  0.9501, Adjusted R-squared:  0.9498 
## F-statistic:  3104 on 3 and 489 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP16" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.384 -3.809 -1.753  4.633 17.775 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   600.849715   0.744203 807.373 <0.0000000000000002 ***
## op_count        2.334641   0.038134  61.222 <0.0000000000000002 ***
## arg0           -0.009659   0.038250  -0.253               0.801    
## op_count:arg0   0.001972   0.001951   1.011               0.313    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.873 on 478 degrees of freedom
## Multiple R-squared:  0.9727, Adjusted R-squared:  0.9726 
## F-statistic:  5686 on 3 and 478 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADD"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.526  -5.413  -3.237   6.706  15.789 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   579.9489447   1.3480367 430.217 <0.0000000000000002 ***
## op_count        2.8222034   0.0682553  41.348 <0.0000000000000002 ***
## arg0           -0.0248637   0.0532171  -0.467               0.641    
## arg1           -0.0079579   0.0509653  -0.156               0.876    
## op_count:arg0   0.0025971   0.0026808   0.969               0.333    
## op_count:arg1  -0.0002471   0.0026353  -0.094               0.925    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.628 on 498 degrees of freedom
## Multiple R-squared:  0.9659, Adjusted R-squared:  0.9656 
## F-statistic:  2825 on 5 and 498 DF,  p-value: < 0.00000000000000022
## 
## [1] "MUL"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.725 -4.737 -2.864  4.986 25.067 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   579.08604113   1.25571478 461.160 <0.0000000000000002 ***
## op_count        3.56906713   0.06511592  54.811 <0.0000000000000002 ***
## arg0            0.02170436   0.05023558   0.432               0.666    
## arg1           -0.00371572   0.05153846  -0.072               0.943    
## op_count:arg0   0.00126162   0.00258593   0.488               0.626    
## op_count:arg1   0.00002024   0.00268670   0.008               0.994    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.377 on 488 degrees of freedom
## Multiple R-squared:  0.9793, Adjusted R-squared:  0.9791 
## F-statistic:  4624 on 5 and 488 DF,  p-value: < 0.00000000000000022
## 
## [1] "SUB"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.196 -5.005 -2.747  5.427 30.466 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   579.5927620   1.3624858 425.394 <0.0000000000000002 ***
## op_count        2.7579557   0.0687780  40.099 <0.0000000000000002 ***
## arg0           -0.0069894   0.0538100  -0.130               0.897    
## arg1           -0.0361458   0.0519204  -0.696               0.487    
## op_count:arg0  -0.0001726   0.0027491  -0.063               0.950    
## op_count:arg1   0.0006169   0.0026575   0.232               0.817    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.611 on 499 degrees of freedom
## Multiple R-squared:  0.9644, Adjusted R-squared:  0.964 
## F-statistic:  2701 on 5 and 499 DF,  p-value: < 0.00000000000000022
## 
## [1] "DIV"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -207.79  -31.59   -4.31   20.08  355.00 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   580.582327  15.140643  38.346 <0.0000000000000002 ***
## op_count        6.598138   0.755205   8.737 <0.0000000000000002 ***
## arg0            0.162477   0.608897   0.267               0.790    
## arg1           -0.206122   0.608573  -0.339               0.735    
## op_count:arg0   0.565559   0.030445  18.576 <0.0000000000000002 ***
## op_count:arg1  -0.002419   0.030044  -0.081               0.936    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 78.61 on 551 degrees of freedom
## Multiple R-squared:  0.8845, Adjusted R-squared:  0.8835 
## F-statistic:   844 on 5 and 551 DF,  p-value: < 0.00000000000000022
## 
## [1] "SDIV" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -220.14  -31.62   -3.88   21.89  328.05 
## 
## Coefficients:
##                Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   578.40239   14.42850  40.088 <0.0000000000000002 ***
## op_count        9.61489    0.71866  13.379 <0.0000000000000002 ***
## arg0           -0.01446    0.58583  -0.025              0.9803    
## arg1            0.09938    0.58926   0.169              0.8661    
## op_count:arg0   0.59519    0.02919  20.392 <0.0000000000000002 ***
## op_count:arg1  -0.06669    0.02937  -2.271              0.0235 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 76.45 on 556 degrees of freedom
## Multiple R-squared:  0.9141, Adjusted R-squared:  0.9133 
## F-statistic:  1184 on 5 and 556 DF,  p-value: < 0.00000000000000022
## 
## [1] "MOD"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -215.01  -36.72   -4.86   23.01  385.01 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   574.129830  15.941266  36.015 <0.0000000000000002 ***
## op_count        7.589832   0.802608   9.456 <0.0000000000000002 ***
## arg0            0.299703   0.623680   0.481               0.631    
## arg1            0.015903   0.630077   0.025               0.980    
## op_count:arg0   0.512596   0.031602  16.220 <0.0000000000000002 ***
## op_count:arg1   0.009852   0.031631   0.311               0.756    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 83.05 on 551 degrees of freedom
## Multiple R-squared:  0.8769, Adjusted R-squared:  0.8758 
## F-statistic: 785.1 on 5 and 551 DF,  p-value: < 0.00000000000000022
## 
## [1] "SMOD" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -210.96  -35.17   -4.89   18.84  338.03 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   577.270870  14.764950  39.097 <0.0000000000000002 ***
## op_count       11.249794   0.737629  15.251 <0.0000000000000002 ***
## arg0            0.216021   0.563431   0.383               0.702    
## arg1           -0.017169   0.612679  -0.028               0.978    
## op_count:arg0   0.465614   0.028543  16.313 <0.0000000000000002 ***
## op_count:arg1  -0.007943   0.030588  -0.260               0.795    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 74.88 on 544 degrees of freedom
## Multiple R-squared:  0.9098, Adjusted R-squared:  0.909 
## F-statistic:  1098 on 5 and 544 DF,  p-value: < 0.00000000000000022
## 
## [1] "EXP"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1573.24   -67.27    -7.66    96.62  1071.80 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   583.62560053  45.26921933  12.892 < 0.0000000000000002 ***
## op_count       15.38777608   2.26637174   6.790      0.0000000000289 ***
## arg0            0.00000236   1.92046289   0.000                1.000    
## arg1            0.01223918   1.88958919   0.006                0.995    
## op_count:arg0   0.19912700   0.09625874   2.069                0.039 *  
## op_count:arg1  35.13345187   0.09406292 373.510 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 259 on 556 degrees of freedom
## Multiple R-squared:  0.9993, Adjusted R-squared:  0.9993 
## F-statistic: 1.623e+05 on 5 and 556 DF,  p-value: < 0.00000000000000022
## 
## [1] "SIGNEXTEND" "revm"      
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.879  -3.961  -1.682   4.262  22.349 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   577.9385056   1.0665274 541.888 <0.0000000000000002 ***
## op_count        2.4008585   0.0541124  44.368 <0.0000000000000002 ***
## arg0           -0.0191173   0.0424845  -0.450               0.653    
## arg1            0.0033480   0.0442296   0.076               0.940    
## op_count:arg0   0.0004963   0.0021667   0.229               0.819    
## op_count:arg1  -0.0003585   0.0022379  -0.160               0.873    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.521 on 494 degrees of freedom
## Multiple R-squared:  0.967,  Adjusted R-squared:  0.9667 
## F-statistic:  2895 on 5 and 494 DF,  p-value: < 0.00000000000000022
## 
## [1] "LT"   "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -13.378  -4.918  -1.845   4.813  28.624 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   578.602928   1.460834 396.077 <0.0000000000000002 ***
## op_count        2.606130   0.074842  34.822 <0.0000000000000002 ***
## arg0            0.018923   0.056065   0.338               0.736    
## arg1           -0.012648   0.060126  -0.210               0.833    
## op_count:arg0  -0.004319   0.002910  -1.484               0.138    
## op_count:arg1  -0.004204   0.003059  -1.374               0.170    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.2 on 492 degrees of freedom
## Multiple R-squared:  0.9466, Adjusted R-squared:  0.9461 
## F-statistic:  1744 on 5 and 492 DF,  p-value: < 0.00000000000000022
## 
## [1] "GT"   "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.875  -4.319  -1.875   4.587  20.770 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   578.819807   1.051893 550.265 < 0.0000000000000002 ***
## op_count        2.622516   0.054573  48.055 < 0.0000000000000002 ***
## arg0            0.011745   0.044740   0.263                0.793    
## arg1           -0.013715   0.044165  -0.311                0.756    
## op_count:arg0  -0.002180   0.002296  -0.949                0.343    
## op_count:arg1  -0.010781   0.002259  -4.773            0.0000024 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.566 on 487 degrees of freedom
## Multiple R-squared:  0.9668, Adjusted R-squared:  0.9664 
## F-statistic:  2835 on 5 and 487 DF,  p-value: < 0.00000000000000022
## 
## [1] "SLT"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.248  -4.959  -1.973   5.088  44.285 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   579.105789   1.624470 356.489 < 0.0000000000000002 ***
## op_count        4.357391   0.080993  53.799 < 0.0000000000000002 ***
## arg0            0.010014   0.060115   0.167                0.868    
## arg1           -0.009199   0.059323  -0.155                0.877    
## op_count:arg0  -0.015203   0.003030  -5.018           0.00000073 ***
## op_count:arg1  -0.014272   0.002988  -4.777           0.00000236 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.573 on 492 degrees of freedom
## Multiple R-squared:  0.9753, Adjusted R-squared:  0.9751 
## F-statistic:  3892 on 5 and 492 DF,  p-value: < 0.00000000000000022
## 
## [1] "SGT"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -15.474  -4.713  -2.440   5.622  23.812 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   577.984463   1.324197 436.479 < 0.0000000000000002 ***
## op_count        4.195808   0.066879  62.737 < 0.0000000000000002 ***
## arg0            0.041294   0.055649   0.742                0.458    
## arg1            0.021166   0.050946   0.415                0.678    
## op_count:arg0  -0.012439   0.002838  -4.383           0.00001429 ***
## op_count:arg1  -0.012666   0.002572  -4.925           0.00000115 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.556 on 494 degrees of freedom
## Multiple R-squared:  0.9808, Adjusted R-squared:  0.9806 
## F-statistic:  5035 on 5 and 494 DF,  p-value: < 0.00000000000000022
## 
## [1] "EQ"   "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.187 -4.275 -1.944  5.098 19.785 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   577.974894   1.051700 549.562 <0.0000000000000002 ***
## op_count        2.494828   0.054186  46.042 <0.0000000000000002 ***
## arg0            0.035351   0.041121   0.860               0.390    
## arg1           -0.013689   0.043545  -0.314               0.753    
## op_count:arg0  -0.001074   0.002106  -0.510               0.610    
## op_count:arg1   0.003415   0.002246   1.521               0.129    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.398 on 485 degrees of freedom
## Multiple R-squared:  0.9714, Adjusted R-squared:  0.9711 
## F-statistic:  3295 on 5 and 485 DF,  p-value: < 0.00000000000000022
## 
## [1] "AND"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.287  -5.311  -3.196   4.806  24.303 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   580.7117769   1.2665075 458.514 <0.0000000000000002 ***
## op_count        2.4057293   0.0655051  36.726 <0.0000000000000002 ***
## arg0            0.0016566   0.0507734   0.033               0.974    
## arg1           -0.0012790   0.0563176  -0.023               0.982    
## op_count:arg0   0.0006575   0.0026031   0.253               0.801    
## op_count:arg1   0.0009837   0.0028784   0.342               0.733    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.746 on 485 degrees of freedom
## Multiple R-squared:  0.9518, Adjusted R-squared:  0.9513 
## F-statistic:  1913 on 5 and 485 DF,  p-value: < 0.00000000000000022
## 
## [1] "OR"   "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.714 -4.064 -2.220  4.661 23.725 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   579.2537865   1.1047206 524.344 <0.0000000000000002 ***
## op_count        2.4167326   0.0570293  42.377 <0.0000000000000002 ***
## arg0           -0.0216681   0.0451782  -0.480               0.632    
## arg1           -0.0090137   0.0441048  -0.204               0.838    
## op_count:arg0   0.0008269   0.0023170   0.357               0.721    
## op_count:arg1   0.0007800   0.0022922   0.340               0.734    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.703 on 487 degrees of freedom
## Multiple R-squared:  0.9655, Adjusted R-squared:  0.9651 
## F-statistic:  2723 on 5 and 487 DF,  p-value: < 0.00000000000000022
## 
## [1] "XOR"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.481 -3.639 -2.191  5.251 12.375 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   578.1003231   0.9242309 625.493 <0.0000000000000002 ***
## op_count        2.3733311   0.0477935  49.658 <0.0000000000000002 ***
## arg0            0.0073060   0.0359936   0.203               0.839    
## arg1            0.0128416   0.0360924   0.356               0.722    
## op_count:arg0  -0.0012286   0.0018768  -0.655               0.513    
## op_count:arg1  -0.0006296   0.0018876  -0.334               0.739    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.753 on 476 degrees of freedom
## Multiple R-squared:  0.9735, Adjusted R-squared:  0.9732 
## F-statistic:  3493 on 5 and 476 DF,  p-value: < 0.00000000000000022
## 
## [1] "BYTE" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.089 -4.969 -2.787  4.715 25.096 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   579.3323227   1.3014497 445.144 <0.0000000000000002 ***
## op_count        2.3441999   0.0681589  34.393 <0.0000000000000002 ***
## arg0           -0.0162000   0.0512175  -0.316               0.752    
## arg1            0.0372265   0.0518346   0.718               0.473    
## op_count:arg0   0.0012638   0.0026801   0.472               0.637    
## op_count:arg1  -0.0001856   0.0026766  -0.069               0.945    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.504 on 491 degrees of freedom
## Multiple R-squared:  0.952,  Adjusted R-squared:  0.9515 
## F-statistic:  1948 on 5 and 491 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHL"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.565  -5.642  -2.870   5.926  28.813 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   579.655651   1.385871 418.261 <0.0000000000000002 ***
## op_count        2.827115   0.071530  39.524 <0.0000000000000002 ***
## arg0            0.032336   0.054959   0.588               0.557    
## arg1           -0.022290   0.056429  -0.395               0.693    
## op_count:arg0  -0.003729   0.002812  -1.326               0.185    
## op_count:arg1   0.002713   0.002936   0.924               0.356    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.126 on 478 degrees of freedom
## Multiple R-squared:  0.9593, Adjusted R-squared:  0.9589 
## F-statistic:  2252 on 5 and 478 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHR"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.685 -4.944 -2.764  5.457 28.722 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   578.408592   1.105551 523.186 <0.0000000000000002 ***
## op_count        3.598871   0.057192  62.926 <0.0000000000000002 ***
## arg0            0.014584   0.048264   0.302               0.763    
## arg1            0.013608   0.048924   0.278               0.781    
## op_count:arg0  -0.002279   0.002512  -0.907               0.365    
## op_count:arg1  -0.001931   0.002482  -0.778               0.437    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.222 on 479 degrees of freedom
## Multiple R-squared:  0.9803, Adjusted R-squared:  0.9801 
## F-statistic:  4773 on 5 and 479 DF,  p-value: < 0.00000000000000022
## 
## [1] "SAR"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.094 -4.737 -2.588  5.468 25.745 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   577.9291232   1.2870546 449.032 <0.0000000000000002 ***
## op_count        3.2413267   0.0657340  49.310 <0.0000000000000002 ***
## arg0            0.0064030   0.0494902   0.129               0.897    
## arg1            0.0300961   0.0477849   0.630               0.529    
## op_count:arg0   0.0008397   0.0024976   0.336               0.737    
## op_count:arg1  -0.0024235   0.0025052  -0.967               0.334    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.067 on 491 degrees of freedom
## Multiple R-squared:  0.9771, Adjusted R-squared:  0.9768 
## F-statistic:  4186 on 5 and 491 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.147 -17.645  -8.465  13.177  91.299 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   538.49306335   4.64730485 115.872 <0.0000000000000002 ***
## op_count        4.18247581   0.24102094  17.353 <0.0000000000000002 ***
## arg0            0.00007922   0.00035986   0.220               0.826    
## arg1           -0.00006533   0.00035201  -0.186               0.853    
## op_count:arg0  -0.00002109   0.00001861  -1.133               0.258    
## op_count:arg1  -0.00001436   0.00001813  -0.792               0.429    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.39 on 555 degrees of freedom
## Multiple R-squared:  0.7808, Adjusted R-squared:  0.7789 
## F-statistic: 395.5 on 5 and 555 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE8" "revm"   
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.490 -16.985  -6.235  10.502  87.179 
## 
## Coefficients:
##                     Estimate     Std. Error t value            Pr(>|t|)    
## (Intercept)   542.6005643728   4.1425739582 130.982 <0.0000000000000002 ***
## op_count        2.7881764889   0.2130658237  13.086 <0.0000000000000002 ***
## arg0            0.0001352236   0.0003465645   0.390               0.697    
## arg1            0.0000003489   0.0003461712   0.001               0.999    
## op_count:arg0   0.0000186396   0.0000179046   1.041               0.298    
## op_count:arg1  -0.0000102763   0.0000180525  -0.569               0.569    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23.8 on 549 degrees of freedom
## Multiple R-squared:  0.6889, Adjusted R-squared:  0.686 
## F-statistic: 243.1 on 5 and 549 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP1" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.9869 -2.8467 -0.3222  2.4667 13.5398 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   371.7890485   0.7596422 489.427 <0.0000000000000002 ***
## op_count        2.6409571   0.0392920  67.214 <0.0000000000000002 ***
## arg0            0.0254139   0.0299630   0.848               0.397    
## arg1            0.0075676   0.0297299   0.255               0.799    
## op_count:arg0  -0.0008929   0.0015522  -0.575               0.565    
## op_count:arg1  -0.0005829   0.0015470  -0.377               0.706    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.159 on 547 degrees of freedom
## Multiple R-squared:  0.9836, Adjusted R-squared:  0.9834 
## F-statistic:  6556 on 5 and 547 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP2" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.9003 -3.0321 -0.3491  2.7900 14.8762 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   373.3509505   0.8085630 461.746 <0.0000000000000002 ***
## op_count        2.5739894   0.0413423  62.260 <0.0000000000000002 ***
## arg0           -0.0310115   0.0329243  -0.942               0.347    
## arg1            0.0213232   0.0310393   0.687               0.492    
## op_count:arg0   0.0013495   0.0016833   0.802               0.423    
## op_count:arg1  -0.0003994   0.0016048  -0.249               0.804    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.29 on 543 degrees of freedom
## Multiple R-squared:  0.9822, Adjusted R-squared:  0.982 
## F-statistic:  5984 on 5 and 543 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP3" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.6509  -2.7447  -0.1902   2.6976  13.5704 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   372.8550633   0.7489747 497.821 <0.0000000000000002 ***
## op_count        2.4883866   0.0386062  64.456 <0.0000000000000002 ***
## arg0            0.0292115   0.0303381   0.963               0.336    
## arg1           -0.0084383   0.0297095  -0.284               0.776    
## op_count:arg0  -0.0003483   0.0015618  -0.223               0.824    
## op_count:arg1   0.0015942   0.0015263   1.045               0.297    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.156 on 540 degrees of freedom
## Multiple R-squared:  0.9821, Adjusted R-squared:  0.9819 
## F-statistic:  5921 on 5 and 540 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP4" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.6903 -2.9869 -0.0338  2.9056 13.1503 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   372.9784844   0.7456925 500.177 <0.0000000000000002 ***
## op_count        2.6052389   0.0385033  67.663 <0.0000000000000002 ***
## arg0           -0.0238099   0.0307345  -0.775               0.439    
## arg1            0.0096281   0.0288345   0.334               0.739    
## op_count:arg0   0.0007395   0.0015824   0.467               0.640    
## op_count:arg1  -0.0003787   0.0014884  -0.254               0.799    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.011 on 547 degrees of freedom
## Multiple R-squared:  0.9847, Adjusted R-squared:  0.9846 
## F-statistic:  7057 on 5 and 547 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP5" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.0918  -2.9646  -0.0172   2.4973  13.2049 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   373.311002   0.776098 481.010 <0.0000000000000002 ***
## op_count        2.667998   0.039906  66.856 <0.0000000000000002 ***
## arg0           -0.001324   0.029144  -0.045               0.964    
## arg1           -0.015087   0.031299  -0.482               0.630    
## op_count:arg0   0.001398   0.001492   0.937               0.349    
## op_count:arg1   0.002661   0.001605   1.658               0.098 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.003 on 542 degrees of freedom
## Multiple R-squared:  0.9862, Adjusted R-squared:  0.9861 
## F-statistic:  7747 on 5 and 542 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP6" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.1370  -3.0179  -0.4232   2.7336  15.4866 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   377.00572945   0.86866147 434.008 <0.0000000000000002 ***
## op_count        2.74574046   0.04436929  61.884 <0.0000000000000002 ***
## arg0            0.00856924   0.03296331   0.260               0.795    
## arg1           -0.00270351   0.03249891  -0.083               0.934    
## op_count:arg0   0.00006177   0.00169084   0.037               0.971    
## op_count:arg1   0.00105712   0.00165997   0.637               0.525    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.405 on 537 degrees of freedom
## Multiple R-squared:  0.9838, Adjusted R-squared:  0.9836 
## F-statistic:  6505 on 5 and 537 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP7" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.454  -2.821   0.080   2.463  12.368 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   380.9486482   0.7563527 503.665 <0.0000000000000002 ***
## op_count        2.5694321   0.0389885  65.902 <0.0000000000000002 ***
## arg0           -0.0011228   0.0305169  -0.037               0.971    
## arg1           -0.0039703   0.0317098  -0.125               0.900    
## op_count:arg0  -0.0002951   0.0015735  -0.188               0.851    
## op_count:arg1   0.0009683   0.0016276   0.595               0.552    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.018 on 547 degrees of freedom
## Multiple R-squared:  0.9843, Adjusted R-squared:  0.9841 
## F-statistic:  6838 on 5 and 547 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP8" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.119  -3.048  -0.082   2.752  11.501 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   373.9636038   0.7413011 504.469 <0.0000000000000002 ***
## op_count        2.6610066   0.0385528  69.022 <0.0000000000000002 ***
## arg0           -0.0208065   0.0305185  -0.682               0.496    
## arg1           -0.0219504   0.0314283  -0.698               0.485    
## op_count:arg0   0.0011395   0.0015840   0.719               0.472    
## op_count:arg1   0.0006051   0.0016050   0.377               0.706    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.226 on 548 degrees of freedom
## Multiple R-squared:  0.9841, Adjusted R-squared:  0.9839 
## F-statistic:  6778 on 5 and 548 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP9" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.6250  -3.2012  -0.3387   2.6783  19.5764 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   373.6068964   0.9106912 410.245 <0.0000000000000002 ***
## op_count        2.7058593   0.0466796  57.967 <0.0000000000000002 ***
## arg0           -0.0171035   0.0350401  -0.488               0.626    
## arg1            0.0034175   0.0337640   0.101               0.919    
## op_count:arg0   0.0002839   0.0018052   0.157               0.875    
## op_count:arg1   0.0008708   0.0017478   0.498               0.619    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.516 on 533 degrees of freedom
## Multiple R-squared:  0.9824, Adjusted R-squared:  0.9822 
## F-statistic:  5950 on 5 and 533 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP10" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.3984  -3.8306  -0.2306   2.9915  14.9911 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   389.904289   0.999537 390.085 <0.0000000000000002 ***
## op_count        2.720076   0.050308  54.069 <0.0000000000000002 ***
## arg0           -0.014180   0.036898  -0.384              0.7009    
## arg1           -0.065848   0.039379  -1.672              0.0951 .  
## op_count:arg0   0.001325   0.001867   0.710              0.4783    
## op_count:arg1   0.002403   0.002016   1.192              0.2339    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.871 on 502 degrees of freedom
## Multiple R-squared:  0.9803, Adjusted R-squared:  0.9801 
## F-statistic:  4990 on 5 and 502 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP11" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.3002  -2.5557  -0.2019   2.5291  12.5583 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   372.4849756   0.7327524 508.337 <0.0000000000000002 ***
## op_count        2.9376666   0.0375802  78.171 <0.0000000000000002 ***
## arg0           -0.0004554   0.0302549  -0.015               0.988    
## arg1            0.0158675   0.0303553   0.523               0.601    
## op_count:arg0  -0.0004760   0.0015708  -0.303               0.762    
## op_count:arg1  -0.0008570   0.0015765  -0.544               0.587    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.031 on 547 degrees of freedom
## Multiple R-squared:  0.9874, Adjusted R-squared:  0.9873 
## F-statistic:  8572 on 5 and 547 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP12" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.3103 -2.8221 -0.3495  2.8228 12.0587 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   376.064333   0.708696 530.643 <0.0000000000000002 ***
## op_count        2.877073   0.036833  78.112 <0.0000000000000002 ***
## arg0            0.017691   0.028776   0.615               0.539    
## arg1            0.041701   0.029854   1.397               0.163    
## op_count:arg0  -0.002190   0.001501  -1.459               0.145    
## op_count:arg1  -0.001226   0.001556  -0.788               0.431    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.102 on 547 degrees of freedom
## Multiple R-squared:  0.9861, Adjusted R-squared:  0.986 
## F-statistic:  7750 on 5 and 547 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP13" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.8220  -2.6527  -0.3802   2.4525  12.6634 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   377.0698645   0.6836212 551.577 <0.0000000000000002 ***
## op_count        2.5479970   0.0354068  71.964 <0.0000000000000002 ***
## arg0           -0.0180584   0.0286806  -0.630               0.529    
## arg1            0.0056051   0.0282538   0.198               0.843    
## op_count:arg0   0.0016982   0.0014901   1.140               0.255    
## op_count:arg1  -0.0002356   0.0014673  -0.161               0.872    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.897 on 539 degrees of freedom
## Multiple R-squared:  0.9852, Adjusted R-squared:  0.9851 
## F-statistic:  7180 on 5 and 539 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP14" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.4663 -3.1660 -0.1667  2.6145 14.2576 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   373.0768413   0.7686329 485.377 <0.0000000000000002 ***
## op_count        2.7960584   0.0395283  70.736 <0.0000000000000002 ***
## arg0           -0.0169727   0.0330369  -0.514               0.608    
## arg1            0.0310777   0.0321233   0.967               0.334    
## op_count:arg0  -0.0002243   0.0016976  -0.132               0.895    
## op_count:arg1  -0.0014886   0.0016336  -0.911               0.363    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.329 on 541 degrees of freedom
## Multiple R-squared:  0.9842, Adjusted R-squared:  0.9841 
## F-statistic:  6756 on 5 and 541 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP15" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.3203 -2.9053 -0.4521  2.5118 13.3560 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   394.794310   0.811289 486.626 <0.0000000000000002 ***
## op_count        2.555863   0.041184  62.059 <0.0000000000000002 ***
## arg0           -0.009679   0.031009  -0.312              0.7551    
## arg1            0.052926   0.030578   1.731              0.0841 .  
## op_count:arg0  -0.000114   0.001585  -0.072              0.9427    
## op_count:arg1  -0.002902   0.001559  -1.861              0.0633 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.108 on 535 degrees of freedom
## Multiple R-squared:  0.9823, Adjusted R-squared:  0.9822 
## F-statistic:  5945 on 5 and 535 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP16" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.6664  -3.1712  -0.3377   2.4134  17.7316 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   388.9554085   0.8711939 446.462 <0.0000000000000002 ***
## op_count        2.4108623   0.0441614  54.592 <0.0000000000000002 ***
## arg0           -0.0283983   0.0325472  -0.873              0.3833    
## arg1           -0.0003882   0.0352200  -0.011              0.9912    
## op_count:arg0   0.0028235   0.0016511   1.710              0.0878 .  
## op_count:arg1   0.0001942   0.0017899   0.108              0.9136    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.442 on 516 degrees of freedom
## Multiple R-squared:  0.9793, Adjusted R-squared:  0.9791 
## F-statistic:  4878 on 5 and 516 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDMOD" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -886.05  -69.18   -3.94   48.90  777.75 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   579.58320   47.21158  12.276 < 0.0000000000000002 ***
## op_count        3.42233    2.35385   1.454                0.147    
## arg0           -0.18787    1.39437  -0.135                0.893    
## arg1            0.15989    1.45260   0.110                0.912    
## arg2           -0.01129    1.54765  -0.007                0.994    
## op_count:arg0   0.85180    0.06989  12.188 < 0.0000000000000002 ***
## op_count:arg1   0.91436    0.07280  12.560 < 0.0000000000000002 ***
## op_count:arg2  -0.50292    0.07733  -6.504       0.000000000176 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 191.3 on 550 degrees of freedom
## Multiple R-squared:  0.8211, Adjusted R-squared:  0.8188 
## F-statistic: 360.5 on 7 and 550 DF,  p-value: < 0.00000000000000022
## 
## [1] "MULMOD" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -496.34  -66.32   -8.48   49.32  633.47 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   593.68070   34.10858  17.406 < 0.0000000000000002 ***
## op_count        7.03445    1.71437   4.103          0.000046985 ***
## arg0           -0.44130    1.17464  -0.376                0.707    
## arg1           -0.09379    1.17381  -0.080                0.936    
## arg2           -0.07425    1.15092  -0.065                0.949    
## op_count:arg0   0.95842    0.05800  16.525 < 0.0000000000000002 ***
## op_count:arg1   0.87817    0.05869  14.963 < 0.0000000000000002 ***
## op_count:arg2   0.28633    0.05750   4.980          0.000000855 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 153.8 on 544 degrees of freedom
## Multiple R-squared:  0.9283, Adjusted R-squared:  0.9273 
## F-statistic:  1006 on 7 and 544 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATACOPY" "revm"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -118.597  -32.111   -7.085   21.461  267.395 
## 
## Coefficients:
##                    Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept)   540.259253283  10.304499776  52.429 <0.0000000000000002 ***
## op_count        6.925268432   0.523265712  13.235 <0.0000000000000002 ***
## arg0            0.000220871   0.000716777   0.308               0.758    
## arg1           -0.000339802   0.000742307  -0.458               0.647    
## arg2           -0.000250952   0.000680307  -0.369               0.712    
## op_count:arg0  -0.000001036   0.000036445  -0.028               0.977    
## op_count:arg1   0.000024068   0.000037738   0.638               0.524    
## op_count:arg2   0.003248158   0.000034585  93.918 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 49.82 on 572 degrees of freedom
## Multiple R-squared:  0.9908, Adjusted R-squared:  0.9907 
## F-statistic:  8792 on 7 and 572 DF,  p-value: < 0.00000000000000022
## 
## [1] "CODECOPY" "revm"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -158.67  -30.26  -10.05   28.94  192.92 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   561.83642017  11.57634715  48.533 <0.0000000000000002 ***
## op_count        9.61617071   0.59198437  16.244 <0.0000000000000002 ***
## arg0            0.00111951   0.00074356   1.506              0.1327    
## arg1           -0.00048324   0.00073088  -0.661              0.5088    
## arg2           -0.00038903   0.00076554  -0.508              0.6115    
## op_count:arg0  -0.00002952   0.00003811  -0.775              0.4389    
## op_count:arg1  -0.00006668   0.00003755  -1.776              0.0763 .  
## op_count:arg2   0.00309823   0.00003899  79.458 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 52.59 on 582 degrees of freedom
## Multiple R-squared:  0.9892, Adjusted R-squared:  0.9891 
## F-statistic:  7635 on 7 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "RETURNDATACOPY" "revm"
## Warning in summary.lm(model): essentially perfect fit: summary may be unreliable
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##                 Min                  1Q              Median                  3Q 
## -0.0000000000001699 -0.0000000000000276 -0.0000000000000047  0.0000000000000119 
##                 Max 
##  0.0000000000068199 
## 
## Coefficients:
##                                  Estimate                  Std. Error
## (Intercept)   292.00000000000039790393203   0.00000000000005349860764
## op_count       -0.00000000000000229015611   0.00000000000000276265622
## arg0            0.00000000000000000882981   0.00000000000000000420693
## arg1           -0.00000000000000000240034   0.00000000000000000386330
## arg2           -0.00000000000000000899445   0.00000000000000000378421
## op_count:arg0  -0.00000000000000000035319   0.00000000000000000021724
## op_count:arg1   0.00000000000000000009601   0.00000000000000000019950
## op_count:arg2   0.00000000000000000035978   0.00000000000000000019542
##                            t value            Pr(>|t|)    
## (Intercept)   5458085973803366.000 <0.0000000000000002 ***
## op_count                    -0.829              0.4075    
## arg0                         2.099              0.0363 *  
## arg1                        -0.621              0.5346    
## arg2                        -2.377              0.0178 *  
## op_count:arg0               -1.626              0.1045    
## op_count:arg1                0.481              0.6305    
## op_count:arg2                1.841              0.0661 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0000000000002832 on 592 degrees of freedom
## Multiple R-squared:  0.5007, Adjusted R-squared:  0.4948 
## F-statistic: 84.82 on 7 and 592 DF,  p-value: < 0.00000000000000022
## Warning in summary.lm(model): essentially perfect fit: summary may be unreliable
## [1] "DIV"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -261.90  -48.13  -11.98   41.21  360.78 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            436.9046    10.6210   41.14 < 0.0000000000000002 ***
## op_count                12.1071     0.3694   32.77 < 0.0000000000000002 ***
## arg0                     5.4110     0.4530   11.94 < 0.0000000000000002 ***
## arg1                     3.3760     0.4419    7.64   0.0000000000000964 ***
## op_count:expensiveTRUE   7.4412     0.4678   15.91 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 82.93 on 552 degrees of freedom
## Multiple R-squared:  0.8712, Adjusted R-squared:  0.8703 
## F-statistic: 933.5 on 4 and 552 DF,  p-value: < 0.00000000000000022
## 
## [1] "MOD"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -262.16  -50.14   -6.73   44.62  405.13 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            436.4132    11.4344  38.167 < 0.0000000000000002 ***
## op_count                13.1650     0.4042  32.571 < 0.0000000000000002 ***
## arg0                     5.0256     0.4718  10.652 < 0.0000000000000002 ***
## arg1                     3.4165     0.4742   7.204     0.00000000000193 ***
## op_count:expensiveTRUE   6.6226     0.4975  13.311 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 87.75 on 552 degrees of freedom
## Multiple R-squared:  0.8623, Adjusted R-squared:  0.8613 
## F-statistic: 864.3 on 4 and 552 DF,  p-value: < 0.00000000000000022
## 
## [1] "SDIV" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -255.18  -47.40  -14.02   43.78  348.01 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            441.0153    10.3136  42.760 < 0.0000000000000002 ***
## op_count                14.5387     0.3740  38.869 < 0.0000000000000002 ***
## arg0                     5.2015     0.4504  11.549 < 0.0000000000000002 ***
## arg1                     2.9989     0.4409   6.802      0.0000000000267 ***
## op_count:expensiveTRUE   7.9530     0.4597  17.300 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 81.47 on 557 degrees of freedom
## Multiple R-squared:  0.9023, Adjusted R-squared:  0.9016 
## F-statistic:  1286 on 4 and 557 DF,  p-value: < 0.00000000000000022
## 
## [1] "SMOD" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -212.37  -47.27  -10.73   41.62  375.27 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            464.0562    10.6934   43.40 < 0.0000000000000002 ***
## op_count                15.9662     0.3518   45.39 < 0.0000000000000002 ***
## arg0                     4.7765     0.4342   11.00 < 0.0000000000000002 ***
## arg1                     2.5081     0.4544    5.52         0.0000000525 ***
## op_count:expensiveTRUE   5.5801     0.4604   12.12 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 81.11 on 545 degrees of freedom
## Multiple R-squared:  0.894,  Adjusted R-squared:  0.8932 
## F-statistic:  1149 on 4 and 545 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDMOD" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -950.99 -102.36  -17.27   66.83  867.72 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)            272.4924    30.1204   9.047 <0.0000000000000002 ***
## op_count                 9.6852     1.0688   9.062 <0.0000000000000002 ***
## arg0                     8.0595     0.8813   9.145 <0.0000000000000002 ***
## arg1                     9.1398     0.9184   9.952 <0.0000000000000002 ***
## arg2                     0.3962     1.0167   0.390               0.697    
## op_count:expensiveTRUE  21.9140     1.1210  19.549 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 189.7 on 552 degrees of freedom
## Multiple R-squared:  0.8233, Adjusted R-squared:  0.8217 
## F-statistic: 514.6 on 5 and 552 DF,  p-value: < 0.00000000000000022
## 
## [1] "MULMOD" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -422.06 -113.92  -21.88   99.05  845.65 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            103.3492    27.4697   3.762             0.000187 ***
## op_count                27.8311     1.2110  22.982 < 0.0000000000000002 ***
## arg0                    10.6671     0.8922  11.955 < 0.0000000000000002 ***
## arg1                    10.0156     0.8928  11.219 < 0.0000000000000002 ***
## arg2                     9.2059     0.9112  10.103 < 0.0000000000000002 ***
## op_count:expensiveTRUE  16.4387     1.2299  13.366 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 185.8 on 546 degrees of freedom
## Multiple R-squared:  0.8949, Adjusted R-squared:  0.894 
## F-statistic: 929.9 on 5 and 546 DF,  p-value: < 0.00000000000000022
proceed_with_opcodes = unique(first_pass[which(first_pass$has_impacting == 'TRUE'), 'opcode'])

models_with_args_automatic = first_pass[which(first_pass$has_impacting == 'TRUE'), c('opcode', 'env')]
models_with_expensive_automatic = first_pass[which(!is.na(first_pass$expensive_ns)), c('opcode', 'env')]

first_pass[which(first_pass$has_impacting == 'TRUE'), ]
##          opcode  env has_significant has_impacting estimate_marginal_ns
## 26          DIV revm            TRUE          TRUE     6.59813831183103
## 27         SDIV revm            TRUE          TRUE     9.61489038110278
## 28          MOD revm            TRUE          TRUE     7.58983240683187
## 29         SMOD revm            TRUE          TRUE     11.2497935242536
## 30          EXP revm            TRUE          TRUE     15.3877760768352
## 62       ADDMOD revm            TRUE          TRUE     3.42232916262161
## 63       MULMOD revm            TRUE          TRUE     7.03445025225489
## 64 CALLDATACOPY revm            TRUE          TRUE     6.92526843170865
## 65     CODECOPY revm            TRUE          TRUE     9.61617071032377
##              arg0_ns           arg1_ns             arg2_ns     expensive_ns
## 26 0.565559437245124              <NA>                <NA>  7.4411977916547
## 27 0.595188150328579              <NA>                <NA> 7.95304115084793
## 28 0.512595716230183              <NA>                <NA>  6.6225636265391
## 29 0.465613812224098              <NA>                <NA>             <NA>
## 30              <NA>   35.133451871341                <NA>             <NA>
## 62              <NA> 0.914357862683964                <NA> 21.9140019413379
## 63 0.958420714710887 0.878170949869229                <NA> 16.4386842908286
## 64              <NA>              <NA> 0.00324815767990504             <NA>
## 65              <NA>              <NA> 0.00309823088630512             <NA>
##                arg0_ns_raw            arg1_ns_raw         arg2_ns_raw
## 26       0.565559437245124   -0.00241905446431549                <NA>
## 27       0.595188150328579    -0.0666929401571928                <NA>
## 28       0.512595716230183    0.00985196760977073                <NA>
## 29       0.465613812224098   -0.00794251961244524                <NA>
## 30       0.199126997414683        35.133451871341                <NA>
## 62        0.85180210148405      0.914357862683964   -0.50292407970811
## 63       0.958420714710887      0.878170949869229   0.286331944449526
## 64 -0.00000103585949809118  0.0000240683762641382 0.00324815767990504
## 65  -0.0000295190588730632 -0.0000666776821671564 0.00309823088630512
##    expensive_ns_raw
## 26  7.4411977916547
## 27 7.95304115084793
## 28  6.6225636265391
## 29    5.58011146072
## 30             <NA>
## 62 21.9140019413379
## 63 16.4386842908286
## 64             <NA>
## 65             <NA>
##                                                                                arg0_ns_p
## 26          0.00000000000000000000000000000000000000000000000000000000000356462080776181
## 27 0.00000000000000000000000000000000000000000000000000000000000000000000195267240103145
## 28                      0.00000000000000000000000000000000000000000000000117768267045346
## 29                     0.000000000000000000000000000000000000000000000000541222507731715
## 30                                                                    0.0390392884885951
## 62                                        0.00000000000000000000000000000203856388810974
## 63                    0.0000000000000000000000000000000000000000000000000520282291590146
## 64                                                                     0.977335263595949
## 65                                                                     0.438906299736389
##                                                     arg1_ns_p
## 26                                          0.935854857474513
## 27                                         0.0235185439403219
## 28                                          0.755568124386772
## 29                                          0.795220032552234
## 30                                                          0
## 62           0.0000000000000000000000000000000543998736482254
## 63 0.00000000000000000000000000000000000000000120872236735034
## 64                                          0.523879935225416
## 65                                         0.0762762860516619
##                     arg2_ns_p
## 26                       <NA>
## 27                       <NA>
## 28                       <NA>
## 29                       <NA>
## 30                       <NA>
## 62 0.000000000176417102430788
## 63    0.000000854946098072246
## 64                          0
## 65      1.27679851274989e-314
##                                                                       expensive_ns_p
## 26                    0.000000000000000000000000000000000000000000000034999990739389
## 27            0.00000000000000000000000000000000000000000000000000000549949820477157
## 28                               0.0000000000000000000000000000000000293761076143241
## 29                                    0.00000000000000000000000000000421431567627815
## 30                                                                              <NA>
## 62 0.0000000000000000000000000000000000000000000000000000000000000000457785370168952
## 63                               0.0000000000000000000000000000000000188580300419877
## 64                                                                              <NA>
## 65                                                                              <NA>

We inspect the automatic choice of models, but then coerce the choice to a fixed list. We drop the division OPCODEs (DIV etc.), because their arguments only seem to have an indirect impact via the fact that x / y is trivial if x < y. This makes the DIV(x, y) appear costlier for large x and cheaper for large y.

models_with_args = data.frame(opcode="EXP", env=env, arg=1)
first_pass$arg1_ns[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env] <- first_pass$arg1_ns_raw[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CALLDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CODECOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="RETURNDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env]

models_with_expensive = data.frame(opcode="DIV", env=env)
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SDIV", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="ADDMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MULMOD", env=env))

Detailed analysis for selected OPCODEs

We go through all the OPCODEs which turned out to have impacting arguments in the automatic discrimination procedure, and we plot some validation plots to inspect these relationships.

# Takes the results data frame and checks which argument indices (0, 1, etc.)
# turned out to be impacting
get_impact_args_for <- function(df, opcode, env) {
  if (opcode %in% nullary_opcodes) {
    return(c())
  }
  args = c()
  for (n in 0:2) {
    argname = paste0('arg', n, '_ns')
    if (!is.na(df[which(df$opcode==opcode & df$env==env), argname])) {
      args = c(n, args)
    }
  }
  return(rev(args))
}

# same as `get_impact_args_for` but gets all the argument indices
get_args_for <- function(df, opcode, env) {
  if (opcode %in% unary_opcodes) {
    c(0)
  } else if (opcode %in% binary_opcodes) {
    c(0, 1)
  } else if (opcode %in% ternary_opcodes) {
    c(0, 1, 2)
  }
}

# Builds a final model formula to estimate, based on whether the arguments
# came out impactful from the automatic discrimination process.
get_model_formula_for <- function(df, opcode, env) {
  args = get_args_for(df, opcode, env)
  argnames = paste0('arg', args)
  args_formula = paste0(argnames, collapse=' + ')
  
  impact_args = get_impact_args_for(df, opcode, env)
  if (opcode %in% nullary_opcodes) {
    as.formula('measure_total_time_ns ~ op_count')
  } else if (is.null(impact_args)) {
    as.formula(paste0('measure_total_time_ns ~ op_count +  ', args_formula))
  } else {
  arg_op_count_names = paste0('arg', impact_args, ':op_count')
  arg_op_counts_formula = paste0(arg_op_count_names, collapse=' + ')
  as.formula(paste0('measure_total_time_ns ~ op_count +  ', args_formula, ' + ', arg_op_counts_formula))
  }
}

# Same as `get_model_formula_for` but gauged towards the division OPCODEs specifically.
get_expensive_model_formula_for <- function(df, opcode, env) {
  args = get_args_for(df, opcode, env)
  argnames = paste0('arg', args)
  args_formula = paste0(argnames, collapse=' + ')
  as.formula(paste0('measure_total_time_ns ~ op_count +  ', args_formula, ' + expensive:op_count'))
}

# Same as `get_model_formula_for` but returns the formula to provide the `aggregate` function with.
get_aggregate_formula_for <- function(df, opcode, env) {
  args = get_args_for(df, opcode, env)
  argnames = paste0('arg', args)
  args_formula = paste0(argnames, collapse=' * ')
  as.formula(paste0('measure_total_time_ns ~ op_count * env * opcode * ', args_formula))
}

# Presents the diagnostic plots for a given slice of the data
plot_model <- function(df, opcode, env, use_mean) {
  if (missing(use_mean)) {
    use_mean = FALSE
  }
  if (use_mean) {
    df = aggregate(get_aggregate_formula_for(df, opcode, env), measurements[which(df$opcode==opcode & df$env==env), ], mean, na.action=na.pass)
  }
  model = arg_lm(df, opcode, env, get_model_formula_for(first_pass, opcode, env))
  print(c(opcode, env))
  print(summary(model))
  
  par(mfrow=c(2,2))
  plot(model)
  
  plot_data = df[which(df$env == env & df$opcode == opcode & df$op_count == max(df$op_count)), ]
  if (opcode %in% binary_opcodes) {
    par(mfrow=c(1,1))
    
    decreasing_colors = heat.colors(nrow(plot_data))
    plot_data=plot_data[order(plot_data$measure_total_time_ns, decreasing=TRUE), ]
    with(plot_data, plot(arg0, arg1, col=decreasing_colors, pch=19))
  }
  title(main=paste(opcode, env))
}

Using the functions defined above, we proceed to plot the diagnostic plots of the arguments models.

for (env in all_envs) {
  for (opcode in proceed_with_opcodes) {
    plot_model(measurements, opcode, env, use_mean=TRUE)
  } 
}
## [1] "DIV"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -207.27  -32.57   -4.37   20.18  350.27 
## 
## Coefficients:
##                Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   586.09741   13.27566  44.148 <0.0000000000000002 ***
## op_count        6.42048    0.59087  10.866 <0.0000000000000002 ***
## arg0            0.16373    0.63915   0.256               0.798    
## arg1           -0.54332    0.37303  -1.457               0.146    
## op_count:arg0   0.57701    0.03206  17.999 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 78.76 on 509 degrees of freedom
## Multiple R-squared:  0.8843, Adjusted R-squared:  0.8833 
## F-statistic: 972.1 on 4 and 509 DF,  p-value: < 0.00000000000000022

## [1] "SDIV" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -210.215  -27.679   -2.912   21.685  303.576 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   594.62171   12.89919  46.098 < 0.0000000000000002 ***
## op_count        8.71576    0.57427  15.177 < 0.0000000000000002 ***
## arg0            0.09302    0.60457   0.154              0.87778    
## arg1           -0.97138    0.36348  -2.672              0.00778 ** 
## op_count:arg0   0.58158    0.03009  19.325 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 74.66 on 497 degrees of freedom
## Multiple R-squared:  0.9156, Adjusted R-squared:  0.9149 
## F-statistic:  1347 on 4 and 497 DF,  p-value: < 0.00000000000000022

## [1] "MOD"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -210.75  -32.58   -4.02   22.36  385.85 
## 
## Coefficients:
##                Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   572.90998   13.94968  41.070 <0.0000000000000002 ***
## op_count        7.41997    0.62912  11.794 <0.0000000000000002 ***
## arg0            0.23522    0.63706   0.369               0.712    
## arg1            0.14925    0.39123   0.381               0.703    
## op_count:arg0   0.52918    0.03235  16.360 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 80.64 on 489 degrees of freedom
## Multiple R-squared:  0.883,  Adjusted R-squared:  0.882 
## F-statistic: 922.3 on 4 and 489 DF,  p-value: < 0.00000000000000022

## [1] "SMOD" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -210.57  -33.70   -4.66   16.31  336.04 
## 
## Coefficients:
##                Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   578.32430   12.26078  47.169 <0.0000000000000002 ***
## op_count       10.99580    0.52920  20.778 <0.0000000000000002 ***
## arg0            0.13312    0.56552   0.235               0.814    
## arg1           -0.02126    0.37383  -0.057               0.955    
## op_count:arg0   0.47461    0.02889  16.430 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 74.02 on 510 degrees of freedom
## Multiple R-squared:  0.9127, Adjusted R-squared:  0.912 
## F-statistic:  1332 on 4 and 510 DF,  p-value: < 0.00000000000000022

## [1] "EXP"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1592.32   -64.14    -1.40    92.80  1052.18 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   540.2785    41.9512  12.879 <0.0000000000000002 ***
## op_count       18.1634     1.8830   9.646 <0.0000000000000002 ***
## arg0            2.9507     1.2495   2.362              0.0186 *  
## arg1           -0.2989     2.0253  -0.148              0.8827    
## op_count:arg1  35.1845     0.1007 349.367 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 261.2 on 494 degrees of freedom
## Multiple R-squared:  0.9993, Adjusted R-squared:  0.9993 
## F-statistic: 1.767e+05 on 4 and 494 DF,  p-value: < 0.00000000000000022

## [1] "ADDMOD" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -857.71 -122.78   -2.52  115.04  863.01 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   477.73297   41.96307  11.385 < 0.0000000000000002 ***
## op_count        9.72396    1.62652   5.978     0.00000000404769 ***
## arg0           13.36997    0.97714  13.683 < 0.0000000000000002 ***
## arg1           -0.35492    1.68174  -0.211                0.833    
## arg2           -8.08706    1.07695  -7.509     0.00000000000024 ***
## op_count:arg1   0.95911    0.08417  11.395 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 222.1 on 552 degrees of freedom
## Multiple R-squared:  0.758,  Adjusted R-squared:  0.7558 
## F-statistic: 345.7 on 5 and 552 DF,  p-value: < 0.00000000000000022

## [1] "MULMOD" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -523.11  -77.89  -12.58   56.04  661.46 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   523.59672   31.92445  16.401 < 0.0000000000000002 ***
## op_count       11.58578    1.50288   7.709    0.000000000000061 ***
## arg0           -0.33694    1.21006  -0.278                0.781    
## arg1           -0.29519    1.19939  -0.246                0.806    
## arg2            4.57794    0.70735   6.472    0.000000000216788 ***
## op_count:arg0   0.94425    0.05969  15.819 < 0.0000000000000002 ***
## op_count:arg1   0.88719    0.05997  14.794 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 157.2 on 542 degrees of freedom
## Multiple R-squared:  0.9251, Adjusted R-squared:  0.9243 
## F-statistic:  1116 on 6 and 542 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATACOPY" "revm"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -117.637  -31.901   -7.836   21.407  267.661 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   537.19424542   8.14312893  65.969 <0.0000000000000002 ***
## op_count        7.12318935   0.32931057  21.631 <0.0000000000000002 ***
## arg0            0.00020759   0.00044187   0.470               0.639    
## arg1            0.00003278   0.00045751   0.072               0.943    
## arg2           -0.00023141   0.00067756  -0.342               0.733    
## op_count:arg2   0.00324673   0.00003439  94.402 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 49.75 on 574 degrees of freedom
## Multiple R-squared:  0.9908, Adjusted R-squared:  0.9907 
## F-statistic: 1.234e+04 on 5 and 574 DF,  p-value: < 0.00000000000000022

## [1] "CODECOPY" "revm"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -153.530  -31.334   -8.539   29.108  201.408 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   574.29730576   9.09095785  63.172 < 0.0000000000000002 ***
## op_count        8.79580998   0.35914850  24.491 < 0.0000000000000002 ***
## arg0            0.00067052   0.00046549   1.440              0.15028    
## arg1           -0.00149388   0.00045919  -3.253              0.00121 ** 
## arg2           -0.00054072   0.00076215  -0.709              0.47832    
## op_count:arg2   0.00310791   0.00003869  80.320 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 52.66 on 584 degrees of freedom
## Multiple R-squared:  0.9892, Adjusted R-squared:  0.9891 
## F-statistic: 1.066e+04 on 5 and 584 DF,  p-value: < 0.00000000000000022

Producing the final estimates

We’d like to only estimate using the arg-variables in models, where this actually matters to avoid spurious impact of insignificant variables.

We’ll estimate a model with only those argument variables, where they turned out impacting. For those where no argument variable was impacting, we’ll only estimate the marginal increase (corresponding to the constant cost of an OPCODE).

# `results_df` is assumed to have the columns as the `estimates` data frame has (see below)
add_non_arg_model_estimates <- function(model, results_df, env, opcode) {
  pure_op_count_coeff = summary(model)$coefficients["op_count", 1]
  args_ns = c(NA, NA, NA)
  args_ns_stderr = c(NA, NA, NA)
  results_df[nrow(results_df) + 1, ] = c(opcode, env, FALSE, FALSE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
  return(results_df)
}
add_arg_model_estimates <- function(model, opcode, env, results_df, df) {
  all_coefficients = summary(model)$coefficients
  arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
  pure_op_count_coeff = all_coefficients["op_count", 1]
  # will be filled if any is impacting
  args_ns = c(NA, NA, NA)
  args_ns_stderr = c(NA, NA, NA)
  
  impact_args = get_impact_args_for(df, opcode, env)
  arg_op_count_names = paste0('op_count:arg', impact_args)

  args_ns[impact_args + 1] = all_coefficients[arg_op_count_names, 'Estimate']
  args_ns_stderr[impact_args + 1] = all_coefficients[arg_op_count_names, 'Std. Error']
  results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
  return(results_df)
}
add_expensive_model_estimates <- function(model, opcode, env, results_df, df) {
  all_coefficients = summary(model)$coefficients
  pure_op_count_coeff = all_coefficients["op_count", 1]
  args_ns = c(NA, NA, NA)
  args_ns_stderr = c(NA, NA, NA)
  expensive =  all_coefficients['op_count:expensiveTRUE', 'Estimate']
  expensive_stderr = all_coefficients['op_count:expensiveTRUE', 'Std. Error']
  results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, expensive, args_ns_stderr, expensive_stderr)
  return(results_df)
}
estimates = data.frame(matrix(ncol = 13, nrow = 0))
colnames(estimates) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
                         'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns', 'arg0_ns_stderr', 'arg1_ns_stderr', 'arg2_ns_stderr', 'expensive_ns_stderr')

for (env in all_envs) {
  for (opcode in all_opcodes) {
    is_modeled_with_args = nrow(merge(data.frame(opcode=opcode, env=env), models_with_args)) > 0
    is_modeled_with_expensive = nrow(merge(data.frame(opcode=opcode, env=env), models_with_expensive)) > 0
    if (is_modeled_with_expensive) {
      model = arg_lm(measurements, opcode, env, get_expensive_model_formula_for(first_pass, opcode, env))
      estimates = add_expensive_model_estimates(model, opcode, env, estimates, first_pass)
    } else if (is_modeled_with_args) {
      model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
      estimates = add_arg_model_estimates(model, opcode, env, estimates, first_pass)
    } else {
      model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
      estimates = add_non_arg_model_estimates(model, estimates, env, opcode)
    }
    print(c(opcode, env))
    print(summary(model))
  }
}
## [1] "ADD"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.243  -5.392  -3.273   6.724  15.797 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 579.32285    0.91766 631.306 <0.0000000000000002 ***
## op_count      2.86163    0.02407 118.889 <0.0000000000000002 ***
## arg0          0.01546    0.03311   0.467               0.641    
## arg1         -0.01167    0.03189  -0.366               0.715    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.621 on 500 degrees of freedom
## Multiple R-squared:  0.9659, Adjusted R-squared:  0.9657 
## F-statistic:  4718 on 3 and 500 DF,  p-value: < 0.00000000000000022
## 
## [1] "MUL"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.013  -4.814  -2.831   4.984  25.065 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 578.780438   0.873290 662.759 <0.0000000000000002 ***
## op_count      3.589878   0.023615 152.018 <0.0000000000000002 ***
## arg0          0.040799   0.031477   1.296               0.196    
## arg1         -0.003624   0.032365  -0.112               0.911    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.365 on 490 degrees of freedom
## Multiple R-squared:  0.9793, Adjusted R-squared:  0.9792 
## F-statistic:  7735 on 3 and 490 DF,  p-value: < 0.00000000000000022
## 
## [1] "SUB"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.246 -5.068 -2.735  5.427 30.466 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 579.478139   0.931260 622.251 <0.0000000000000002 ***
## op_count      2.765530   0.023758 116.404 <0.0000000000000002 ***
## arg0         -0.009515   0.033557  -0.284               0.777    
## arg1         -0.026913   0.033098  -0.813               0.417    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.598 on 501 degrees of freedom
## Multiple R-squared:  0.9644, Adjusted R-squared:  0.9641 
## F-statistic:  4518 on 3 and 501 DF,  p-value: < 0.00000000000000022
## 
## [1] "DIV"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -261.90  -48.13  -11.98   41.21  360.78 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            436.9046    10.6210   41.14 < 0.0000000000000002 ***
## op_count                12.1071     0.3694   32.77 < 0.0000000000000002 ***
## arg0                     5.4110     0.4530   11.94 < 0.0000000000000002 ***
## arg1                     3.3760     0.4419    7.64   0.0000000000000964 ***
## op_count:expensiveTRUE   7.4412     0.4678   15.91 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 82.93 on 552 degrees of freedom
## Multiple R-squared:  0.8712, Adjusted R-squared:  0.8703 
## F-statistic: 933.5 on 4 and 552 DF,  p-value: < 0.00000000000000022
## 
## [1] "SDIV" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -255.18  -47.40  -14.02   43.78  348.01 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            441.0153    10.3136  42.760 < 0.0000000000000002 ***
## op_count                14.5387     0.3740  38.869 < 0.0000000000000002 ***
## arg0                     5.2015     0.4504  11.549 < 0.0000000000000002 ***
## arg1                     2.9989     0.4409   6.802      0.0000000000267 ***
## op_count:expensiveTRUE   7.9530     0.4597  17.300 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 81.47 on 557 degrees of freedom
## Multiple R-squared:  0.9023, Adjusted R-squared:  0.9016 
## F-statistic:  1286 on 4 and 557 DF,  p-value: < 0.00000000000000022
## 
## [1] "MOD"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -262.16  -50.14   -6.73   44.62  405.13 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            436.4132    11.4344  38.167 < 0.0000000000000002 ***
## op_count                13.1650     0.4042  32.571 < 0.0000000000000002 ***
## arg0                     5.0256     0.4718  10.652 < 0.0000000000000002 ***
## arg1                     3.4165     0.4742   7.204     0.00000000000193 ***
## op_count:expensiveTRUE   6.6226     0.4975  13.311 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 87.75 on 552 degrees of freedom
## Multiple R-squared:  0.8623, Adjusted R-squared:  0.8613 
## F-statistic: 864.3 on 4 and 552 DF,  p-value: < 0.00000000000000022
## 
## [1] "SMOD" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -212.37  -47.27  -10.73   41.62  375.27 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            464.0562    10.6934   43.40 < 0.0000000000000002 ***
## op_count                15.9662     0.3518   45.39 < 0.0000000000000002 ***
## arg0                     4.7765     0.4342   11.00 < 0.0000000000000002 ***
## arg1                     2.5081     0.4544    5.52         0.0000000525 ***
## op_count:expensiveTRUE   5.5801     0.4604   12.12 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 81.11 on 545 degrees of freedom
## Multiple R-squared:  0.894,  Adjusted R-squared:  0.8932 
## F-statistic:  1149 on 4 and 545 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDMOD" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -950.99 -102.36  -17.27   66.83  867.72 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)            272.4924    30.1204   9.047 <0.0000000000000002 ***
## op_count                 9.6852     1.0688   9.062 <0.0000000000000002 ***
## arg0                     8.0595     0.8813   9.145 <0.0000000000000002 ***
## arg1                     9.1398     0.9184   9.952 <0.0000000000000002 ***
## arg2                     0.3962     1.0167   0.390               0.697    
## op_count:expensiveTRUE  21.9140     1.1210  19.549 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 189.7 on 552 degrees of freedom
## Multiple R-squared:  0.8233, Adjusted R-squared:  0.8217 
## F-statistic: 514.6 on 5 and 552 DF,  p-value: < 0.00000000000000022
## 
## [1] "MULMOD" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -422.06 -113.92  -21.88   99.05  845.65 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            103.3492    27.4697   3.762             0.000187 ***
## op_count                27.8311     1.2110  22.982 < 0.0000000000000002 ***
## arg0                    10.6671     0.8922  11.955 < 0.0000000000000002 ***
## arg1                    10.0156     0.8928  11.219 < 0.0000000000000002 ***
## arg2                     9.2059     0.9112  10.103 < 0.0000000000000002 ***
## op_count:expensiveTRUE  16.4387     1.2299  13.366 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 185.8 on 546 degrees of freedom
## Multiple R-squared:  0.8949, Adjusted R-squared:  0.894 
## F-statistic: 929.9 on 5 and 546 DF,  p-value: < 0.00000000000000022
## 
## [1] "EXP"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1568.87   -71.73    -0.50    98.24  1069.40 
## 
## Coefficients:
##                Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   535.97618   39.08558  13.713 <0.0000000000000002 ***
## op_count       18.37172    1.75322  10.479 <0.0000000000000002 ***
## arg0            3.17046    1.16068   2.732              0.0065 ** 
## arg1           -0.22732    1.89158  -0.120              0.9044    
## op_count:arg1  35.14924    0.09403 373.816 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 259.7 on 557 degrees of freedom
## Multiple R-squared:  0.9993, Adjusted R-squared:  0.9993 
## F-statistic: 2.017e+05 on 4 and 557 DF,  p-value: < 0.00000000000000022
## 
## [1] "SIGNEXTEND" "revm"      
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.949  -3.905  -1.692   4.266  22.389 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 577.913171   0.731329 790.224 <0.0000000000000002 ***
## op_count      2.402378   0.019947 120.437 <0.0000000000000002 ***
## arg0         -0.011516   0.026456  -0.435               0.664    
## arg1         -0.002179   0.027536  -0.079               0.937    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.51 on 496 degrees of freedom
## Multiple R-squared:  0.967,  Adjusted R-squared:  0.9668 
## F-statistic:  4844 on 3 and 496 DF,  p-value: < 0.00000000000000022
## 
## [1] "LT"   "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -13.468  -5.328  -1.705   4.796  29.046 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 580.68016    1.01316 573.138 <0.0000000000000002 ***
## op_count      2.46814    0.02651  93.091 <0.0000000000000002 ***
## arg0         -0.04481    0.03567  -1.256               0.210    
## arg1         -0.07665    0.03741  -2.049               0.041 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.214 on 494 degrees of freedom
## Multiple R-squared:  0.9462, Adjusted R-squared:  0.9458 
## F-statistic:  2894 on 3 and 494 DF,  p-value: < 0.00000000000000022
## 
## [1] "GT"   "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.561  -4.563  -1.517   4.621  20.752 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 581.98429    0.76084 764.921 < 0.0000000000000002 ***
## op_count      2.40671    0.02072 116.164 < 0.0000000000000002 ***
## arg0         -0.01713    0.02948  -0.581                0.561    
## arg1         -0.17608    0.02857  -6.162         0.0000000015 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.694 on 489 degrees of freedom
## Multiple R-squared:  0.9651, Adjusted R-squared:  0.9649 
## F-statistic:  4506 on 3 and 489 DF,  p-value: < 0.00000000000000022
## 
## [1] "SLT"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.325  -5.528  -0.536   5.354  40.838 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 587.00865    1.13950 515.147 < 0.0000000000000002 ***
## op_count      3.85641    0.02889 133.473 < 0.0000000000000002 ***
## arg0         -0.22794    0.03882  -5.871        0.00000000796 ***
## arg1         -0.23440    0.03779  -6.203        0.00000000117 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.884 on 494 degrees of freedom
## Multiple R-squared:  0.9732, Adjusted R-squared:  0.973 
## F-statistic:  5972 on 3 and 494 DF,  p-value: < 0.00000000000000022
## 
## [1] "SGT"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.768  -5.294  -1.580   5.808  22.667 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 584.35310    0.93827 622.801 < 0.0000000000000002 ***
## op_count      3.78642    0.02489 152.099 < 0.0000000000000002 ***
## arg0         -0.15161    0.03628  -4.179          0.000034639 ***
## arg1         -0.17754    0.03296  -5.386          0.000000111 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.824 on 496 degrees of freedom
## Multiple R-squared:  0.9791, Adjusted R-squared:  0.9789 
## F-statistic:  7733 on 3 and 496 DF,  p-value: < 0.00000000000000022
## 
## [1] "EQ"   "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.837 -4.454 -2.037  5.106 20.073 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 577.36817    0.72024 801.635 <0.0000000000000002 ***
## op_count      2.53432    0.01978 128.118 <0.0000000000000002 ***
## arg0          0.01979    0.02593   0.763               0.446    
## arg1          0.03751    0.02740   1.369               0.172    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.402 on 487 degrees of freedom
## Multiple R-squared:  0.9713, Adjusted R-squared:  0.9711 
## F-statistic:  5484 on 3 and 487 DF,  p-value: < 0.00000000000000022
## 
## [1] "ISZERO" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.743 -3.894 -1.885  5.676 12.698 
## 
## Coefficients:
##              Estimate Std. Error  t value            Pr(>|t|)    
## (Intercept) 578.75439    0.54734 1057.405 <0.0000000000000002 ***
## op_count      2.17168    0.01919  113.178 <0.0000000000000002 ***
## arg0         -0.00116    0.02607   -0.044               0.965    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.116 on 474 degrees of freedom
## Multiple R-squared:  0.9643, Adjusted R-squared:  0.9642 
## F-statistic:  6405 on 2 and 474 DF,  p-value: < 0.00000000000000022
## 
## [1] "AND"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.994 -5.404 -3.366  4.808 24.310 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 580.30325    0.88473 655.911 <0.0000000000000002 ***
## op_count      2.43309    0.02483  97.988 <0.0000000000000002 ***
## arg0          0.01131    0.03200   0.354               0.724    
## arg1          0.01346    0.03528   0.382               0.703    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.734 on 487 degrees of freedom
## Multiple R-squared:  0.9517, Adjusted R-squared:  0.9514 
## F-statistic:  3201 on 3 and 487 DF,  p-value: < 0.00000000000000022
## 
## [1] "OR"   "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.833 -4.036 -2.178  4.666 23.726 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 578.870631   0.772138 749.698 <0.0000000000000002 ***
## op_count      2.442502   0.020908 116.820 <0.0000000000000002 ***
## arg0         -0.009362   0.028531  -0.328               0.743    
## arg1          0.002409   0.027955   0.086               0.931    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.692 on 489 degrees of freedom
## Multiple R-squared:  0.9654, Adjusted R-squared:  0.9652 
## F-statistic:  4554 on 3 and 489 DF,  p-value: < 0.00000000000000022
## 
## [1] "XOR"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.638 -3.636 -2.041  5.260 12.372 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 578.580326   0.629749 918.747 <0.0000000000000002 ***
## op_count      2.341813   0.017708 132.244 <0.0000000000000002 ***
## arg0         -0.011279   0.022963  -0.491               0.624    
## arg1          0.002733   0.023128   0.118               0.906    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.746 on 478 degrees of freedom
## Multiple R-squared:  0.9734, Adjusted R-squared:  0.9733 
## F-statistic:  5839 on 3 and 478 DF,  p-value: < 0.00000000000000022
## 
## [1] "NOT"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.081 -4.014 -2.053  3.966 15.925 
## 
## Coefficients:
##               Estimate Std. Error  t value            Pr(>|t|)    
## (Intercept) 578.191880   0.533669 1083.429 <0.0000000000000002 ***
## op_count      2.199801   0.017943  122.598 <0.0000000000000002 ***
## arg0         -0.005532   0.023820   -0.232               0.816    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.783 on 478 degrees of freedom
## Multiple R-squared:  0.9692, Adjusted R-squared:  0.9691 
## F-statistic:  7518 on 2 and 478 DF,  p-value: < 0.00000000000000022
## 
## [1] "BYTE" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.895 -4.929 -2.852  4.721 25.094 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 579.068707   0.909065 636.994 <0.0000000000000002 ***
## op_count      2.362403   0.023904  98.828 <0.0000000000000002 ***
## arg0          0.002489   0.032572   0.076               0.939    
## arg1          0.034000   0.032582   1.043               0.297    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.492 on 493 degrees of freedom
## Multiple R-squared:  0.952,  Adjusted R-squared:  0.9517 
## F-statistic:  3258 on 3 and 493 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHL"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.121  -5.628  -3.087   5.930  28.808 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 579.94144    0.96590 600.418 <0.0000000000000002 ***
## op_count      2.80949    0.02650 106.011 <0.0000000000000002 ***
## arg0         -0.02408    0.03470  -0.694               0.488    
## arg1          0.01769    0.03597   0.492               0.623    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.131 on 480 degrees of freedom
## Multiple R-squared:  0.9591, Adjusted R-squared:  0.9588 
## F-statistic:  3748 on 3 and 480 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHR"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.545 -4.930 -2.671  5.476 28.718 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 579.38430    0.77024 752.213 <0.0000000000000002 ***
## op_count      3.53413    0.02290 154.352 <0.0000000000000002 ***
## arg0         -0.01948    0.03106  -0.627               0.531    
## arg1         -0.01690    0.03054  -0.553               0.580    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.219 on 481 degrees of freedom
## Multiple R-squared:  0.9803, Adjusted R-squared:  0.9801 
## F-statistic:  7963 on 3 and 481 DF,  p-value: < 0.00000000000000022
## 
## [1] "SAR"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.153 -4.835 -2.597  5.470 25.872 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 578.354434   0.872625 662.775 <0.0000000000000002 ***
## op_count      3.211861   0.022182 144.799 <0.0000000000000002 ***
## arg0          0.019070   0.030645   0.622               0.534    
## arg1         -0.005407   0.030265  -0.179               0.858    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.061 on 493 degrees of freedom
## Multiple R-squared:  0.977,  Adjusted R-squared:  0.9769 
## F-statistic:  6989 on 3 and 493 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDRESS" "revm"   
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.487  -6.205  -4.487   9.154  19.154 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 225.2053     0.5548   406.0 <0.0000000000000002 ***
## op_count      5.8427     0.0294   198.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.122 on 513 degrees of freedom
## Multiple R-squared:  0.9872, Adjusted R-squared:  0.9872 
## F-statistic: 3.95e+04 on 1 and 513 DF,  p-value: < 0.00000000000000022
## 
## [1] "ORIGIN" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.459  -6.650  -2.650   5.945  31.945 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.64998    0.67455   333.0 <0.0000000000000002 ***
## op_count      5.76031    0.03474   165.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.16 on 572 degrees of freedom
## Multiple R-squared:  0.9796, Adjusted R-squared:  0.9796 
## F-statistic: 2.75e+04 on 1 and 572 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLER" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.018 -5.018 -3.689  6.647 18.647 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.68909    0.47155   476.5 <0.0000000000000002 ***
## op_count      6.71095    0.02467   272.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.893 on 532 degrees of freedom
## Multiple R-squared:  0.9929, Adjusted R-squared:  0.9928 
## F-statistic: 7.399e+04 on 1 and 532 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLVALUE" "revm"     
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.901 -4.770 -2.770  6.165 13.165 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.76982    0.39784   562.5 <0.0000000000000002 ***
## op_count      2.73771    0.02058   133.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.774 on 527 degrees of freedom
## Multiple R-squared:  0.9711, Adjusted R-squared:  0.971 
## F-statistic: 1.77e+04 on 1 and 527 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATALOAD" "revm"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -72.12 -38.70 -18.79  37.15 155.70 
## 
## Coefficients:
##                Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept) 835.4002959   4.7142393 177.208 <0.0000000000000002 ***
## op_count      3.0992753   0.1597015  19.407 <0.0000000000000002 ***
## arg0          0.0005157   0.0004054   1.272               0.204    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 47.67 on 591 degrees of freedom
## Multiple R-squared:  0.3902, Adjusted R-squared:  0.3882 
## F-statistic: 189.1 on 2 and 591 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATASIZE" "revm"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.139 -4.870 -3.139  6.495 13.495 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.86993    0.41242   542.8 <0.0000000000000002 ***
## op_count      2.37564    0.02155   110.3 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.106 on 544 degrees of freedom
## Multiple R-squared:  0.9572, Adjusted R-squared:  0.9571 
## F-statistic: 1.216e+04 on 1 and 544 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATACOPY" "revm"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -117.637  -31.901   -7.836   21.407  267.661 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   537.19424542   8.14312893  65.969 <0.0000000000000002 ***
## op_count        7.12318935   0.32931057  21.631 <0.0000000000000002 ***
## arg0            0.00020759   0.00044187   0.470               0.639    
## arg1            0.00003278   0.00045751   0.072               0.943    
## arg2           -0.00023141   0.00067756  -0.342               0.733    
## op_count:arg2   0.00324673   0.00003439  94.402 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 49.75 on 574 degrees of freedom
## Multiple R-squared:  0.9908, Adjusted R-squared:  0.9907 
## F-statistic: 1.234e+04 on 5 and 574 DF,  p-value: < 0.00000000000000022
## 
## [1] "CODESIZE" "revm"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.906 -4.693 -2.906  7.200 12.200 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.90638    0.41048   545.5 <0.0000000000000002 ***
## op_count      2.39289    0.02137   112.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.971 on 525 degrees of freedom
## Multiple R-squared:  0.9598, Adjusted R-squared:  0.9597 
## F-statistic: 1.254e+04 on 1 and 525 DF,  p-value: < 0.00000000000000022
## 
## [1] "CODECOPY" "revm"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -153.530  -31.334   -8.539   29.108  201.408 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   574.29730576   9.09095785  63.172 < 0.0000000000000002 ***
## op_count        8.79580998   0.35914850  24.491 < 0.0000000000000002 ***
## arg0            0.00067052   0.00046549   1.440              0.15028    
## arg1           -0.00149388   0.00045919  -3.253              0.00121 ** 
## arg2           -0.00054072   0.00076215  -0.709              0.47832    
## op_count:arg2   0.00310791   0.00003869  80.320 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 52.66 on 584 degrees of freedom
## Multiple R-squared:  0.9892, Adjusted R-squared:  0.9891 
## F-statistic: 1.066e+04 on 5 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "GASPRICE" "revm"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.938 -4.938 -2.938  7.020 14.020 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.02166    0.42000   533.4 <0.0000000000000002 ***
## op_count      2.73055    0.02172   125.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.174 on 544 degrees of freedom
## Multiple R-squared:  0.9667, Adjusted R-squared:  0.9667 
## F-statistic: 1.58e+04 on 1 and 544 DF,  p-value: < 0.00000000000000022
## 
## [1] "RETURNDATASIZE" "revm"          
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.843 -4.348 -2.843  6.405 13.405 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.34759    0.37629   593.5 <0.0000000000000002 ***
## op_count      2.28318    0.01948   117.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.514 on 533 degrees of freedom
## Multiple R-squared:  0.9626, Adjusted R-squared:  0.9626 
## F-statistic: 1.373e+04 on 1 and 533 DF,  p-value: < 0.00000000000000022
## Warning in summary.lm(model): essentially perfect fit: summary may be unreliable
## [1] "RETURNDATACOPY" "revm"
## Warning in summary.lm(model): essentially perfect fit: summary may be unreliable
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##                 Min                  1Q              Median                  3Q 
## -0.0000000000001229 -0.0000000000000279 -0.0000000000000051  0.0000000000000122 
##                 Max 
##  0.0000000000068519 
## 
## Coefficients:
##                                 Estimate                 Std. Error
## (Intercept)   292.0000000000003979039320   0.0000000000000435395541
## op_count       -0.0000000000000039107328   0.0000000000000018249520
## arg0            0.0000000000000000035319   0.0000000000000000026624
## arg1           -0.0000000000000000009601   0.0000000000000000024450
## arg2           -0.0000000000000000087549   0.0000000000000000037825
## op_count:arg2   0.0000000000000000003438   0.0000000000000000001952
##                            t value            Pr(>|t|)    
## (Intercept)   6706545487355970.000 <0.0000000000000002 ***
## op_count                    -2.143              0.0325 *  
## arg0                         1.327              0.1852    
## arg1                        -0.393              0.6947    
## arg2                        -2.315              0.0210 *  
## op_count:arg2                1.761              0.0787 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0000000000002834 on 594 degrees of freedom
## Multiple R-squared:  0.5024, Adjusted R-squared:  0.4982 
## F-statistic:   120 on 5 and 594 DF,  p-value: < 0.00000000000000022
## 
## [1] "COINBASE" "revm"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.323  -5.665  -3.323   7.506  17.506 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.66493    0.49627   452.7 <0.0000000000000002 ***
## op_count      6.05528    0.02615   231.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.26 on 519 degrees of freedom
## Multiple R-squared:  0.9904, Adjusted R-squared:  0.9904 
## F-statistic: 5.362e+04 on 1 and 519 DF,  p-value: < 0.00000000000000022
## 
## [1] "TIMESTAMP" "revm"     
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.885 -4.703 -2.885  6.706 11.706 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.70284    0.39470   566.8 <0.0000000000000002 ***
## op_count      2.70607    0.02056   131.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.776 on 532 degrees of freedom
## Multiple R-squared:  0.9702, Adjusted R-squared:  0.9702 
## F-statistic: 1.733e+04 on 1 and 532 DF,  p-value: < 0.00000000000000022
## 
## [1] "NUMBER" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.320 -4.683 -2.683  6.498 12.498 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.68300    0.38845   575.8 <0.0000000000000002 ***
## op_count      2.65457    0.02027   130.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.777 on 541 degrees of freedom
## Multiple R-squared:  0.9694, Adjusted R-squared:  0.9694 
## F-statistic: 1.714e+04 on 1 and 541 DF,  p-value: < 0.00000000000000022
## 
## [1] "DIFFICULTY" "revm"      
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.022 -4.597 -3.022  7.191 12.191 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.59744    0.39852   561.1 <0.0000000000000002 ***
## op_count      2.88080    0.02062   139.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.918 on 547 degrees of freedom
## Multiple R-squared:  0.9727, Adjusted R-squared:  0.9727 
## F-statistic: 1.951e+04 on 1 and 547 DF,  p-value: < 0.00000000000000022
## 
## [1] "GASLIMIT" "revm"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.642 -4.642 -2.739  6.309 13.309 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.7389     0.4027   555.5 <0.0000000000000002 ***
## op_count      2.6635     0.0208   128.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.919 on 544 degrees of freedom
## Multiple R-squared:  0.9679, Adjusted R-squared:  0.9678 
## F-statistic: 1.64e+04 on 1 and 544 DF,  p-value: < 0.00000000000000022
## 
## [1] "CHAINID" "revm"   
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.841 -4.675 -2.675  6.242 13.242 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.67494    0.38702   577.9 <0.0000000000000002 ***
## op_count      2.67220    0.02013   132.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.673 on 538 degrees of freedom
## Multiple R-squared:  0.9704, Adjusted R-squared:  0.9703 
## F-statistic: 1.762e+04 on 1 and 538 DF,  p-value: < 0.00000000000000022
## 
## [1] "SELFBALANCE" "revm"       
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.515 -4.157 -2.157  5.164 12.164 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.15665    0.34865   640.1 <0.0000000000000002 ***
## op_count      2.24529    0.01793   125.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.052 on 531 degrees of freedom
## Multiple R-squared:  0.9673, Adjusted R-squared:  0.9672 
## F-statistic: 1.569e+04 on 1 and 531 DF,  p-value: < 0.00000000000000022
## 
## [1] "POP"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.5524  -4.0902  -0.3184   3.8904  17.0106 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 374.42654    0.56069  667.79 <0.0000000000000002 ***
## op_count      1.97967    0.01913  103.46 <0.0000000000000002 ***
## arg0         -0.01201    0.02505   -0.48               0.632    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.43 on 534 degrees of freedom
## Multiple R-squared:  0.9525, Adjusted R-squared:  0.9523 
## F-statistic:  5353 on 2 and 534 DF,  p-value: < 0.00000000000000022
## 
## [1] "MLOAD" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -83.51 -42.44 -16.98  38.29 161.42 
## 
## Coefficients:
##                Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept) 855.6543066   5.3174766 160.914 <0.0000000000000002 ***
## op_count      3.4554550   0.1744011  19.813 <0.0000000000000002 ***
## arg0         -0.0005107   0.0004522  -1.129               0.259    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 51.92 on 582 degrees of freedom
## Multiple R-squared:  0.4039, Adjusted R-squared:  0.4018 
## F-statistic: 197.1 on 2 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.309 -18.185  -7.933  13.795  90.176 
## 
## Coefficients:
##                Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept) 542.8238506   3.2072884 169.247 <0.0000000000000002 ***
## op_count      3.8930379   0.0876173  44.432 <0.0000000000000002 ***
## arg0         -0.0002356   0.0002279  -1.033               0.302    
## arg1         -0.0002808   0.0002213  -1.269               0.205    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.38 on 557 degrees of freedom
## Multiple R-squared:  0.7802, Adjusted R-squared:  0.779 
## F-statistic: 658.9 on 3 and 557 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE8" "revm"   
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.997 -17.088  -6.281  10.547  86.547 
## 
## Coefficients:
##                Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept) 541.4949453   2.9258195 185.075 <0.0000000000000002 ***
## op_count      2.8609933   0.0821192  34.840 <0.0000000000000002 ***
## arg0          0.0004126   0.0002216   1.862              0.0632 .  
## arg1         -0.0001518   0.0002204  -0.689              0.4913    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23.78 on 551 degrees of freedom
## Multiple R-squared:  0.6881, Adjusted R-squared:  0.6864 
## F-statistic: 405.3 on 3 and 551 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMP" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.954 -5.482 -3.954  8.282 18.282 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 194.48197    0.48920   397.5 <0.0000000000000002 ***
## op_count      2.81572    0.02532   111.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.186 on 535 degrees of freedom
## Multiple R-squared:  0.9585, Adjusted R-squared:  0.9585 
## F-statistic: 1.237e+04 on 1 and 535 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMPI" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -27.28 -15.61 -10.91  22.73  47.11 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 557.53748    2.04064 273.217 <0.0000000000000002 ***
## op_count      3.95360    0.07238  54.621 <0.0000000000000002 ***
## arg0          0.01526    0.09385   0.163               0.871    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.22 on 525 degrees of freedom
## Multiple R-squared:  0.8504, Adjusted R-squared:  0.8498 
## F-statistic:  1492 on 2 and 525 DF,  p-value: < 0.00000000000000022
## 
## [1] "PC"   "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.912 -5.225 -3.225  8.431 12.431 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.22528    0.45316   494.8 <0.0000000000000002 ***
## op_count      2.62289    0.02344   111.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.643 on 535 degrees of freedom
## Multiple R-squared:  0.959,  Adjusted R-squared:  0.9589 
## F-statistic: 1.252e+04 on 1 and 535 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSIZE" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.216 -5.216 -3.216  7.774 12.774 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.23578    0.42967   521.9 <0.0000000000000002 ***
## op_count      2.46602    0.02191   112.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.242 on 538 degrees of freedom
## Multiple R-squared:  0.9593, Adjusted R-squared:  0.9592 
## F-statistic: 1.267e+04 on 1 and 538 DF,  p-value: < 0.00000000000000022
## 
## [1] "GAS"  "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.243 -4.640 -2.243  6.059 14.059 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.63967    0.38649   578.6 <0.0000000000000002 ***
## op_count      2.28678    0.01996   114.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.673 on 552 degrees of freedom
## Multiple R-squared:  0.9596, Adjusted R-squared:  0.9596 
## F-statistic: 1.312e+04 on 1 and 552 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMPDEST" "revm"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.258 -16.258   5.725   6.725   7.759 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 44.240841   0.655292  67.513 <0.0000000000000002 ***
## op_count     0.001127   0.033184   0.034               0.973    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.943 on 545 degrees of freedom
## Multiple R-squared:  2.115e-06,  Adjusted R-squared:  -0.001833 
## F-statistic: 0.001152 on 1 and 545 DF,  p-value: 0.9729
## 
## [1] "PUSH1" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.233 -4.475 -2.475  6.146 13.146 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.47467    0.38171   585.5 <0.0000000000000002 ***
## op_count      2.29193    0.02018   113.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.634 on 528 degrees of freedom
## Multiple R-squared:  0.9607, Adjusted R-squared:  0.9606 
## F-statistic: 1.29e+04 on 1 and 528 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH2" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.541 -4.541 -3.102  7.178 14.178 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.10221    0.42461   527.8 <0.0000000000000002 ***
## op_count      2.31462    0.02178   106.3 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.164 on 532 degrees of freedom
## Multiple R-squared:  0.955,  Adjusted R-squared:  0.9549 
## F-statistic: 1.129e+04 on 1 and 532 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH3" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.788 -4.745 -2.788  6.734 12.734 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.78766    0.40251   556.0 <0.0000000000000002 ***
## op_count      2.36524    0.02083   113.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.83 on 520 degrees of freedom
## Multiple R-squared:  0.9612, Adjusted R-squared:  0.9611 
## F-statistic: 1.289e+04 on 1 and 520 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH4" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.857 -4.743 -2.857  7.200 13.200 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.74267    0.42081   531.7 <0.0000000000000002 ***
## op_count      2.33716    0.02136   109.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.129 on 531 degrees of freedom
## Multiple R-squared:  0.9575, Adjusted R-squared:  0.9574 
## F-statistic: 1.197e+04 on 1 and 531 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH5" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.866 -4.798 -2.866  6.668 12.668 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.79829    0.40352   554.6 <0.0000000000000002 ***
## op_count      2.36893    0.02059   115.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.85 on 543 degrees of freedom
## Multiple R-squared:  0.9606, Adjusted R-squared:  0.9605 
## F-statistic: 1.323e+04 on 1 and 543 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH6" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.097 -4.955 -2.955  6.974 12.974 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.95550    0.40575   552.0 <0.0000000000000002 ***
## op_count      2.40470    0.02066   116.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.91 on 541 degrees of freedom
## Multiple R-squared:  0.9616, Adjusted R-squared:  0.9615 
## F-statistic: 1.355e+04 on 1 and 541 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH7" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.127 -5.016 -3.016  6.928 13.928 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.12736    0.41900   534.9 <0.0000000000000002 ***
## op_count      2.46297    0.02169   113.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.164 on 540 degrees of freedom
## Multiple R-squared:  0.9598, Adjusted R-squared:  0.9597 
## F-statistic: 1.29e+04 on 1 and 540 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH8" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.817 -5.335 -3.335  7.424 14.424 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.33455    0.43857   511.5 <0.0000000000000002 ***
## op_count      2.41608    0.02258   107.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.569 on 566 degrees of freedom
## Multiple R-squared:  0.9529, Adjusted R-squared:  0.9528 
## F-statistic: 1.145e+04 on 1 and 566 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH9" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.372 -5.372 -3.372  7.538 14.539 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.37175    0.44627   502.8 <0.0000000000000002 ***
## op_count      2.40598    0.02283   105.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.495 on 536 degrees of freedom
## Multiple R-squared:  0.954,  Adjusted R-squared:  0.9539 
## F-statistic: 1.111e+04 on 1 and 536 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH10" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.317 -4.317 -2.526  6.079 12.079 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.52564    0.38455   581.3 <0.0000000000000002 ***
## op_count      2.35970    0.02006   117.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.708 on 541 degrees of freedom
## Multiple R-squared:  0.9624, Adjusted R-squared:  0.9623 
## F-statistic: 1.384e+04 on 1 and 541 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH11" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.037 -4.998 -3.037  7.483 12.483 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.03668    0.41829   535.6 <0.0000000000000002 ***
## op_count      2.43204    0.02158   112.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.175 on 553 degrees of freedom
## Multiple R-squared:  0.9583, Adjusted R-squared:  0.9582 
## F-statistic: 1.27e+04 on 1 and 553 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH12" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.072 -4.144 -3.072  6.892 11.892 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.07210    0.40266   556.5 <0.0000000000000002 ***
## op_count      2.40238    0.02076   115.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.867 on 535 degrees of freedom
## Multiple R-squared:  0.9616, Adjusted R-squared:  0.9615 
## F-statistic: 1.339e+04 on 1 and 535 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH13" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.771 -4.771 -3.378  6.426 13.426 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.37800    0.42638   526.2 <0.0000000000000002 ***
## op_count      2.41309    0.02184   110.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.17 on 541 degrees of freedom
## Multiple R-squared:  0.9576, Adjusted R-squared:  0.9575 
## F-statistic: 1.221e+04 on 1 and 541 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH14" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.888 -4.710 -2.888  6.701 13.701 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.71027    0.40420   553.5 <0.0000000000000002 ***
## op_count      2.43925    0.02097   116.3 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.009 on 549 degrees of freedom
## Multiple R-squared:  0.961,  Adjusted R-squared:  0.9609 
## F-statistic: 1.353e+04 on 1 and 549 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH15" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.922 -4.754 -2.922  7.912 10.662 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.75400    0.42618   525.0 <0.0000000000000002 ***
## op_count      2.63893    0.02179   121.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.194 on 514 degrees of freedom
## Multiple R-squared:  0.9661, Adjusted R-squared:  0.9661 
## F-statistic: 1.466e+04 on 1 and 514 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH16" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.873 -4.517 -2.517  6.305 13.305 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.51703    0.37233   600.3 <0.0000000000000002 ***
## op_count      2.34521    0.01913   122.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.489 on 545 degrees of freedom
## Multiple R-squared:  0.965,  Adjusted R-squared:  0.9649 
## F-statistic: 1.503e+04 on 1 and 545 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH17" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.444 -5.340 -3.340  7.109 15.108 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.33956    0.45544   492.6 <0.0000000000000002 ***
## op_count      2.50346    0.02307   108.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.498 on 527 degrees of freedom
## Multiple R-squared:  0.9572, Adjusted R-squared:  0.9571 
## F-statistic: 1.177e+04 on 1 and 527 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH18" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.863 -4.863 -3.526  6.806 13.806 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.52620    0.45608   492.3 <0.0000000000000002 ***
## op_count      2.51121    0.02341   107.3 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.481 on 526 degrees of freedom
## Multiple R-squared:  0.9563, Adjusted R-squared:  0.9562 
## F-statistic: 1.151e+04 on 1 and 526 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH19" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.338 -5.118 -3.228  7.772 12.772 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.33843    0.44047   509.3 <0.0000000000000002 ***
## op_count      2.65931    0.02255   117.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.444 on 546 degrees of freedom
## Multiple R-squared:  0.9622, Adjusted R-squared:  0.9621 
## F-statistic: 1.39e+04 on 1 and 546 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH20" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.331 -4.749 -3.331  7.460 12.460 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.33062    0.42966   522.1 <0.0000000000000002 ***
## op_count      2.48061    0.02192   113.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.201 on 541 degrees of freedom
## Multiple R-squared:  0.9595, Adjusted R-squared:  0.9594 
## F-statistic: 1.28e+04 on 1 and 541 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH21" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.629 -4.628 -3.393  7.489 12.489 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.39282    0.43220   519.2 <0.0000000000000002 ***
## op_count      2.67452    0.02208   121.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.317 on 544 degrees of freedom
## Multiple R-squared:  0.9643, Adjusted R-squared:  0.9642 
## F-statistic: 1.468e+04 on 1 and 544 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH22" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.067 -4.640 -3.067  6.646 12.646 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.06721    0.42188   531.1 <0.0000000000000002 ***
## op_count      2.68576    0.02161   124.3 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.072 on 524 degrees of freedom
## Multiple R-squared:  0.9672, Adjusted R-squared:  0.9671 
## F-statistic: 1.545e+04 on 1 and 524 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH23" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.804 -4.804 -2.804  6.616 12.616 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.96475    0.39220   571.0 <0.0000000000000002 ***
## op_count      2.69463    0.02028   132.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.773 on 542 degrees of freedom
## Multiple R-squared:  0.9702, Adjusted R-squared:  0.9702 
## F-statistic: 1.765e+04 on 1 and 542 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH24" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.450 -5.160 -3.450  8.195 13.195 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.1602     0.4441   504.7 <0.0000000000000002 ***
## op_count      2.5097     0.0229   109.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.536 on 543 degrees of freedom
## Multiple R-squared:  0.9567, Adjusted R-squared:  0.9567 
## F-statistic: 1.201e+04 on 1 and 543 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH25" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.139 -4.735 -3.139  7.563 12.563 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.73462    0.40974     546 <0.0000000000000002 ***
## op_count      2.58016    0.02115     122 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.027 on 541 degrees of freedom
## Multiple R-squared:  0.9649, Adjusted R-squared:  0.9649 
## F-statistic: 1.489e+04 on 1 and 541 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH26" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.844 -4.844 -3.249  7.454 14.454 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.24923    0.43679   513.4 <0.0000000000000002 ***
## op_count      2.68648    0.02263   118.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.441 on 547 degrees of freedom
## Multiple R-squared:  0.9626, Adjusted R-squared:  0.9626 
## F-statistic: 1.409e+04 on 1 and 547 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH27" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.596 -4.596 -2.939  7.232 12.232 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.93899    0.41022   545.9 <0.0000000000000002 ***
## op_count      2.72191    0.02108   129.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.007 on 541 degrees of freedom
## Multiple R-squared:  0.9686, Adjusted R-squared:  0.9685 
## F-statistic: 1.667e+04 on 1 and 541 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH28" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.878 -4.645 -2.878  7.238 12.238 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.87810    0.40844   548.1 <0.0000000000000002 ***
## op_count      2.65891    0.02109   126.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.993 on 539 degrees of freedom
## Multiple R-squared:  0.9672, Adjusted R-squared:  0.9671 
## F-statistic: 1.59e+04 on 1 and 539 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH29" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.188 -4.760 -2.760  6.526 11.526 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.7596     0.3957   565.4 <0.0000000000000002 ***
## op_count      2.7143     0.0209   129.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.81 on 530 degrees of freedom
## Multiple R-squared:  0.9695, Adjusted R-squared:  0.9695 
## F-statistic: 1.687e+04 on 1 and 530 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH30" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.699 -4.693 -2.699  5.804 11.804 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.69914    0.38244   584.9 <0.0000000000000002 ***
## op_count      2.69980    0.02002   134.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.615 on 528 degrees of freedom
## Multiple R-squared:  0.9718, Adjusted R-squared:  0.9717 
## F-statistic: 1.819e+04 on 1 and 528 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH31" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.009 -4.936 -3.010  7.527 12.527 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.00950    0.41649   537.9 <0.0000000000000002 ***
## op_count      2.76423    0.02148   128.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.061 on 531 degrees of freedom
## Multiple R-squared:  0.9689, Adjusted R-squared:  0.9689 
## F-statistic: 1.657e+04 on 1 and 531 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH32" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.116 -5.116 -3.157  7.864 11.864 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.11604    0.43590   514.2 <0.0000000000000002 ***
## op_count      2.66803    0.02278   117.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.373 on 527 degrees of freedom
## Multiple R-squared:  0.963,  Adjusted R-squared:  0.9629 
## F-statistic: 1.372e+04 on 1 and 527 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP1" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.664 -4.716 -2.291  4.741 23.865 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 578.20518    0.68919 838.967 <0.0000000000000002 ***
## op_count      2.36473    0.02282 103.631 <0.0000000000000002 ***
## arg0          0.01911    0.03045   0.628               0.531    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.167 on 483 degrees of freedom
## Multiple R-squared:  0.957,  Adjusted R-squared:  0.9568 
## F-statistic:  5370 on 2 and 483 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP2" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.171 -3.968 -1.804  3.975 24.769 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 578.10436    0.53857 1073.41 <0.0000000000000002 ***
## op_count      2.34107    0.01904  122.94 <0.0000000000000002 ***
## arg0         -0.01503    0.02424   -0.62               0.535    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.157 on 484 degrees of freedom
## Multiple R-squared:  0.969,  Adjusted R-squared:  0.9688 
## F-statistic:  7558 on 2 and 484 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP3" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.509 -3.659 -2.381  4.697 22.847 
## 
## Coefficients:
##              Estimate Std. Error  t value            Pr(>|t|)    
## (Intercept) 578.76620    0.55847 1036.350 <0.0000000000000002 ***
## op_count      2.31245    0.01872  123.500 <0.0000000000000002 ***
## arg0         -0.02140    0.02590   -0.826               0.409    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.102 on 486 degrees of freedom
## Multiple R-squared:  0.9691, Adjusted R-squared:  0.969 
## F-statistic:  7628 on 2 and 486 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP4" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.708 -4.048 -2.048  4.696 19.795 
## 
## Coefficients:
##              Estimate Std. Error  t value            Pr(>|t|)    
## (Intercept) 578.65871    0.57338 1009.202 <0.0000000000000002 ***
## op_count      2.28828    0.01964  116.531 <0.0000000000000002 ***
## arg0          0.02469    0.02578    0.958               0.339    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.269 on 478 degrees of freedom
## Multiple R-squared:  0.966,  Adjusted R-squared:  0.9659 
## F-statistic:  6790 on 2 and 478 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP5" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.699 -4.054 -1.500  4.016 18.592 
## 
## Coefficients:
##              Estimate Std. Error  t value            Pr(>|t|)    
## (Intercept) 577.89352    0.53313 1083.965 <0.0000000000000002 ***
## op_count      2.31359    0.01804  128.272 <0.0000000000000002 ***
## arg0          0.01529    0.02312    0.662               0.509    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.887 on 487 degrees of freedom
## Multiple R-squared:  0.9713, Adjusted R-squared:  0.9712 
## F-statistic:  8241 on 2 and 487 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP6" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.483 -3.550 -1.975  4.245 12.472 
## 
## Coefficients:
##               Estimate Std. Error  t value            Pr(>|t|)    
## (Intercept) 578.069115   0.526448 1098.055 <0.0000000000000002 ***
## op_count      2.316361   0.017284  134.018 <0.0000000000000002 ***
## arg0         -0.003191   0.023886   -0.134               0.894    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.681 on 485 degrees of freedom
## Multiple R-squared:  0.9737, Adjusted R-squared:  0.9736 
## F-statistic:  8980 on 2 and 485 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP7" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.861 -3.915 -1.942  4.216 27.359 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 582.949026   0.641195  909.16 <0.0000000000000002 ***
## op_count      2.258382   0.021726  103.95 <0.0000000000000002 ***
## arg0         -0.006807   0.029586   -0.23               0.818    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.783 on 478 degrees of freedom
## Multiple R-squared:  0.9576, Adjusted R-squared:  0.9575 
## F-statistic:  5404 on 2 and 478 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP8" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.114 -4.210 -2.190  4.435 21.473 
## 
## Coefficients:
##              Estimate Std. Error  t value            Pr(>|t|)    
## (Intercept) 586.74846    0.55664 1054.081 <0.0000000000000002 ***
## op_count      2.24548    0.01952  115.017 <0.0000000000000002 ***
## arg0          0.01923    0.02684    0.717               0.474    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.263 on 480 degrees of freedom
## Multiple R-squared:  0.965,  Adjusted R-squared:  0.9649 
## F-statistic:  6617 on 2 and 480 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP9" "revm"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.036 -3.431 -2.160  4.424 13.902 
## 
## Coefficients:
##              Estimate Std. Error  t value            Pr(>|t|)    
## (Intercept) 578.80497    0.49800 1162.265 <0.0000000000000002 ***
## op_count      2.26253    0.01707  132.514 <0.0000000000000002 ***
## arg0         -0.02081    0.02320   -0.897                0.37    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.553 on 476 degrees of freedom
## Multiple R-squared:  0.9736, Adjusted R-squared:  0.9735 
## F-statistic:  8780 on 2 and 476 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP10" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.364 -4.416 -2.847  4.515 27.300 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 578.74387    0.69052 838.127 <0.0000000000000002 ***
## op_count      2.30630    0.02329  99.019 <0.0000000000000002 ***
## arg0          0.01724    0.02982   0.578               0.563    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.259 on 484 degrees of freedom
## Multiple R-squared:  0.953,  Adjusted R-squared:  0.9528 
## F-statistic:  4903 on 2 and 484 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP11" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.436 -3.680 -2.358  4.492 16.572 
## 
## Coefficients:
##              Estimate Std. Error  t value            Pr(>|t|)    
## (Intercept) 591.70399    0.56785 1042.006 <0.0000000000000002 ***
## op_count      2.33067    0.01907  122.192 <0.0000000000000002 ***
## arg0         -0.01178    0.02602   -0.453               0.651    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.111 on 473 degrees of freedom
## Multiple R-squared:  0.9693, Adjusted R-squared:  0.9692 
## F-statistic:  7478 on 2 and 473 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP12" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.322 -3.764 -1.896  4.020 16.806 
## 
## Coefficients:
##              Estimate Std. Error  t value            Pr(>|t|)    
## (Intercept) 578.44127    0.50438 1146.825 <0.0000000000000002 ***
## op_count      2.28275    0.01744  130.864 <0.0000000000000002 ***
## arg0         -0.01881    0.02271   -0.828               0.408    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.745 on 489 degrees of freedom
## Multiple R-squared:  0.9722, Adjusted R-squared:  0.9721 
## F-statistic:  8563 on 2 and 489 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP13" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.320 -4.535 -2.552  4.302 23.256 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 582.23894    0.70144 830.059 <0.0000000000000002 ***
## op_count      2.36085    0.02306 102.375 <0.0000000000000002 ***
## arg0          0.01159    0.03075   0.377               0.706    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.274 on 493 degrees of freedom
## Multiple R-squared:  0.9551, Adjusted R-squared:  0.9549 
## F-statistic:  5240 on 2 and 493 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP14" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.637 -4.332 -2.555  4.628 24.769 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 583.26218    0.64667 901.953 <0.0000000000000002 ***
## op_count      2.25602    0.02254 100.072 <0.0000000000000002 ***
## arg0          0.01170    0.03027   0.387               0.699    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.009 on 480 degrees of freedom
## Multiple R-squared:  0.9543, Adjusted R-squared:  0.9541 
## F-statistic:  5012 on 2 and 480 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP15" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.120 -4.890 -1.899  3.889 26.105 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 578.95423    0.75591 765.908 <0.0000000000000002 ***
## op_count      2.34100    0.02424  96.567 <0.0000000000000002 ***
## arg0         -0.00919    0.03423  -0.268               0.788    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.541 on 490 degrees of freedom
## Multiple R-squared:  0.9501, Adjusted R-squared:  0.9499 
## F-statistic:  4663 on 2 and 490 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP16" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.594 -3.867 -1.789  4.626 18.057 
## 
## Coefficients:
##              Estimate Std. Error  t value            Pr(>|t|)    
## (Intercept) 600.33146    0.53930 1113.160 <0.0000000000000002 ***
## op_count      2.36854    0.01814  130.582 <0.0000000000000002 ***
## arg0          0.02058    0.02382    0.864               0.388    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.873 on 479 degrees of freedom
## Multiple R-squared:  0.9727, Adjusted R-squared:  0.9726 
## F-statistic:  8527 on 2 and 479 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP1" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.9596 -2.7900 -0.3508  2.4845 13.5401 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 372.144770   0.528239 704.500 <0.0000000000000002 ***
## op_count      2.617155   0.014435 181.303 <0.0000000000000002 ***
## arg0          0.012072   0.019049   0.634               0.527    
## arg1         -0.001122   0.019014  -0.059               0.953    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.153 on 549 degrees of freedom
## Multiple R-squared:  0.9836, Adjusted R-squared:  0.9835 
## F-statistic: 1.096e+04 on 3 and 549 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP2" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.0071  -3.0907  -0.2978   2.7865  14.8701 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 373.11210    0.55366 673.901 <0.0000000000000002 ***
## op_count      2.58929    0.01495 173.164 <0.0000000000000002 ***
## arg0         -0.01044    0.02052  -0.509               0.611    
## arg1          0.01552    0.01964   0.791               0.430    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.285 on 545 degrees of freedom
## Multiple R-squared:  0.9822, Adjusted R-squared:  0.9821 
## F-statistic:  9996 on 3 and 545 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP3" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.5809  -2.7874  -0.2457   2.7206  13.6519 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 372.54409    0.52432 710.531 <0.0000000000000002 ***
## op_count      2.50906    0.01457 172.190 <0.0000000000000002 ***
## arg0          0.02361    0.01917   1.232               0.219    
## arg1          0.01582    0.01861   0.850               0.396    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.153 on 542 degrees of freedom
## Multiple R-squared:  0.982,  Adjusted R-squared:  0.9819 
## F-statistic:  9883 on 3 and 542 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP4" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.7064 -2.9362  0.0339  2.8519 13.1485 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 372.88617    0.51679 721.542 <0.0000000000000002 ***
## op_count      2.61149    0.01388 188.095 <0.0000000000000002 ***
## arg0         -0.01265    0.01939  -0.652               0.515    
## arg1          0.00390    0.01827   0.213               0.831    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.005 on 549 degrees of freedom
## Multiple R-squared:  0.9847, Adjusted R-squared:  0.9846 
## F-statistic: 1.18e+04 on 3 and 549 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP5" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.7939  -2.9280  -0.0384   2.5148  13.1593 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 372.32327    0.53530 695.544 <0.0000000000000002 ***
## op_count      2.73361    0.01391 196.511 <0.0000000000000002 ***
## arg0          0.01974    0.01856   1.064               0.288    
## arg1          0.02515    0.01978   1.272               0.204    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.008 on 544 degrees of freedom
## Multiple R-squared:  0.9861, Adjusted R-squared:  0.986 
## F-statistic: 1.288e+04 on 3 and 544 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP6" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.1545  -3.0397  -0.3612   2.7384  15.4865 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 376.714276   0.593962 634.240 <0.0000000000000002 ***
## op_count      2.764786   0.015309 180.602 <0.0000000000000002 ***
## arg0          0.009468   0.020763   0.456               0.649    
## arg1          0.013412   0.020361   0.659               0.510    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.398 on 539 degrees of freedom
## Multiple R-squared:  0.9837, Adjusted R-squared:  0.9837 
## F-statistic: 1.087e+04 on 3 and 539 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP7" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.3630  -2.8131   0.0053   2.4642  12.3665 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 380.765047   0.521727 729.817 <0.0000000000000002 ***
## op_count      2.581628   0.013942 185.166 <0.0000000000000002 ***
## arg0         -0.005637   0.019256  -0.293                0.77    
## arg1          0.010720   0.019893   0.539                0.59    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.012 on 549 degrees of freedom
## Multiple R-squared:  0.9842, Adjusted R-squared:  0.9842 
## F-statistic: 1.143e+04 on 3 and 549 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP8" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.7524  -3.1534  -0.1447   2.8104  11.5939 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 373.544271   0.514206 726.449 <0.0000000000000002 ***
## op_count      2.689155   0.014593 184.278 <0.0000000000000002 ***
## arg0         -0.003884   0.019473  -0.199               0.842    
## arg1         -0.012677   0.019796  -0.640               0.522    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.221 on 550 degrees of freedom
## Multiple R-squared:  0.9841, Adjusted R-squared:  0.984 
## F-statistic: 1.132e+04 on 3 and 550 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP9" "revm" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.4469  -3.1860  -0.3294   2.6931  19.5783 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 373.31280    0.62272 599.489 <0.0000000000000002 ***
## op_count      2.72537    0.01578 172.756 <0.0000000000000002 ***
## arg0         -0.01272    0.02204  -0.577               0.564    
## arg1          0.01642    0.02145   0.765               0.444    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.508 on 535 degrees of freedom
## Multiple R-squared:  0.9824, Adjusted R-squared:  0.9823 
## F-statistic:  9948 on 3 and 535 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP10" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.0148  -3.7398  -0.1356   3.0816  14.9904 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 388.950219   0.682950 569.515 <0.0000000000000002 ***
## op_count      2.781802   0.017617 157.903 <0.0000000000000002 ***
## arg0          0.006601   0.023002   0.287               0.774    
## arg1         -0.029375   0.024999  -1.175               0.241    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.87 on 504 degrees of freedom
## Multiple R-squared:  0.9802, Adjusted R-squared:  0.9801 
## F-statistic:  8319 on 3 and 504 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP11" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.3170  -2.5961  -0.1884   2.5263  12.5536 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 372.811333   0.510979 729.601 <0.0000000000000002 ***
## op_count      2.915987   0.014077 207.143 <0.0000000000000002 ***
## arg0         -0.007482   0.019151  -0.391               0.696    
## arg1          0.003083   0.019089   0.162               0.872    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.025 on 549 degrees of freedom
## Multiple R-squared:  0.9874, Adjusted R-squared:  0.9873 
## F-statistic: 1.433e+04 on 3 and 549 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP12" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.4718 -2.8296 -0.2964  2.8027 12.1951 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 376.95271    0.49788 757.112 <0.0000000000000002 ***
## op_count      2.81737    0.01433 196.565 <0.0000000000000002 ***
## arg0         -0.01464    0.01846  -0.793               0.428    
## arg1          0.02328    0.01889   1.232               0.218    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.107 on 549 degrees of freedom
## Multiple R-squared:  0.986,  Adjusted R-squared:  0.9859 
## F-statistic: 1.289e+04 on 3 and 549 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP13" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.8676  -2.8414  -0.1767   2.4295  12.6560 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 376.698144   0.481075 783.034 <0.0000000000000002 ***
## op_count      2.573168   0.013572 189.589 <0.0000000000000002 ***
## arg0          0.006969   0.018435   0.378               0.706    
## arg1          0.002178   0.017923   0.122               0.903    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.894 on 541 degrees of freedom
## Multiple R-squared:  0.9852, Adjusted R-squared:  0.9851 
## F-statistic: 1.198e+04 on 3 and 541 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP14" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.4135 -3.2052 -0.2455  2.5695 14.2608 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 373.492736   0.536642 695.982 <0.0000000000000002 ***
## op_count      2.768618   0.015048 183.980 <0.0000000000000002 ***
## arg0         -0.020181   0.020988  -0.962               0.337    
## arg1          0.008245   0.020045   0.411               0.681    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.324 on 543 degrees of freedom
## Multiple R-squared:  0.9842, Adjusted R-squared:  0.9841 
## F-statistic: 1.128e+04 on 3 and 543 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP15" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.8839 -2.7301 -0.6129  2.4036 13.4924 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 395.604061   0.552101 716.543 <0.0000000000000002 ***
## op_count      2.503428   0.014541 172.162 <0.0000000000000002 ***
## arg0         -0.011716   0.019311  -0.607               0.544    
## arg1          0.008355   0.019037   0.439               0.661    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.113 on 537 degrees of freedom
## Multiple R-squared:  0.9822, Adjusted R-squared:  0.9821 
## F-statistic:  9881 on 3 and 537 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP16" "revm"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.3992  -3.2512  -0.3614   2.5301  17.3898 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 388.20546    0.58949 658.539 <0.0000000000000002 ***
## op_count      2.45882    0.01576 156.012 <0.0000000000000002 ***
## arg0          0.01482    0.02048   0.724               0.470    
## arg1          0.00350    0.02209   0.158               0.874    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.446 on 518 degrees of freedom
## Multiple R-squared:  0.9792, Adjusted R-squared:  0.979 
## F-statistic:  8115 on 3 and 518 DF,  p-value: < 0.00000000000000022
estimates
##             opcode  env has_significant has_impacting
## 1              ADD revm           FALSE         FALSE
## 2              MUL revm           FALSE         FALSE
## 3              SUB revm           FALSE         FALSE
## 4              DIV revm            TRUE          TRUE
## 5             SDIV revm            TRUE          TRUE
## 6              MOD revm            TRUE          TRUE
## 7             SMOD revm            TRUE          TRUE
## 8           ADDMOD revm            TRUE          TRUE
## 9           MULMOD revm            TRUE          TRUE
## 10             EXP revm            TRUE          TRUE
## 11      SIGNEXTEND revm           FALSE         FALSE
## 12              LT revm           FALSE         FALSE
## 13              GT revm           FALSE         FALSE
## 14             SLT revm           FALSE         FALSE
## 15             SGT revm           FALSE         FALSE
## 16              EQ revm           FALSE         FALSE
## 17          ISZERO revm           FALSE         FALSE
## 18             AND revm           FALSE         FALSE
## 19              OR revm           FALSE         FALSE
## 20             XOR revm           FALSE         FALSE
## 21             NOT revm           FALSE         FALSE
## 22            BYTE revm           FALSE         FALSE
## 23             SHL revm           FALSE         FALSE
## 24             SHR revm           FALSE         FALSE
## 25             SAR revm           FALSE         FALSE
## 26         ADDRESS revm           FALSE         FALSE
## 27          ORIGIN revm           FALSE         FALSE
## 28          CALLER revm           FALSE         FALSE
## 29       CALLVALUE revm           FALSE         FALSE
## 30    CALLDATALOAD revm           FALSE         FALSE
## 31    CALLDATASIZE revm           FALSE         FALSE
## 32    CALLDATACOPY revm            TRUE          TRUE
## 33        CODESIZE revm           FALSE         FALSE
## 34        CODECOPY revm            TRUE          TRUE
## 35        GASPRICE revm           FALSE         FALSE
## 36  RETURNDATASIZE revm           FALSE         FALSE
## 37  RETURNDATACOPY revm            TRUE          TRUE
## 38        COINBASE revm           FALSE         FALSE
## 39       TIMESTAMP revm           FALSE         FALSE
## 40          NUMBER revm           FALSE         FALSE
## 41      DIFFICULTY revm           FALSE         FALSE
## 42        GASLIMIT revm           FALSE         FALSE
## 43         CHAINID revm           FALSE         FALSE
## 44     SELFBALANCE revm           FALSE         FALSE
## 45             POP revm           FALSE         FALSE
## 46           MLOAD revm           FALSE         FALSE
## 47          MSTORE revm           FALSE         FALSE
## 48         MSTORE8 revm           FALSE         FALSE
## 49            JUMP revm           FALSE         FALSE
## 50           JUMPI revm           FALSE         FALSE
## 51              PC revm           FALSE         FALSE
## 52           MSIZE revm           FALSE         FALSE
## 53             GAS revm           FALSE         FALSE
## 54        JUMPDEST revm           FALSE         FALSE
## 55           PUSH1 revm           FALSE         FALSE
## 56           PUSH2 revm           FALSE         FALSE
## 57           PUSH3 revm           FALSE         FALSE
## 58           PUSH4 revm           FALSE         FALSE
## 59           PUSH5 revm           FALSE         FALSE
## 60           PUSH6 revm           FALSE         FALSE
## 61           PUSH7 revm           FALSE         FALSE
## 62           PUSH8 revm           FALSE         FALSE
## 63           PUSH9 revm           FALSE         FALSE
## 64          PUSH10 revm           FALSE         FALSE
## 65          PUSH11 revm           FALSE         FALSE
## 66          PUSH12 revm           FALSE         FALSE
## 67          PUSH13 revm           FALSE         FALSE
## 68          PUSH14 revm           FALSE         FALSE
## 69          PUSH15 revm           FALSE         FALSE
## 70          PUSH16 revm           FALSE         FALSE
## 71          PUSH17 revm           FALSE         FALSE
## 72          PUSH18 revm           FALSE         FALSE
## 73          PUSH19 revm           FALSE         FALSE
## 74          PUSH20 revm           FALSE         FALSE
## 75          PUSH21 revm           FALSE         FALSE
## 76          PUSH22 revm           FALSE         FALSE
## 77          PUSH23 revm           FALSE         FALSE
## 78          PUSH24 revm           FALSE         FALSE
## 79          PUSH25 revm           FALSE         FALSE
## 80          PUSH26 revm           FALSE         FALSE
## 81          PUSH27 revm           FALSE         FALSE
## 82          PUSH28 revm           FALSE         FALSE
## 83          PUSH29 revm           FALSE         FALSE
## 84          PUSH30 revm           FALSE         FALSE
## 85          PUSH31 revm           FALSE         FALSE
## 86          PUSH32 revm           FALSE         FALSE
## 87            DUP1 revm           FALSE         FALSE
## 88            DUP2 revm           FALSE         FALSE
## 89            DUP3 revm           FALSE         FALSE
## 90            DUP4 revm           FALSE         FALSE
## 91            DUP5 revm           FALSE         FALSE
## 92            DUP6 revm           FALSE         FALSE
## 93            DUP7 revm           FALSE         FALSE
## 94            DUP8 revm           FALSE         FALSE
## 95            DUP9 revm           FALSE         FALSE
## 96           DUP10 revm           FALSE         FALSE
## 97           DUP11 revm           FALSE         FALSE
## 98           DUP12 revm           FALSE         FALSE
## 99           DUP13 revm           FALSE         FALSE
## 100          DUP14 revm           FALSE         FALSE
## 101          DUP15 revm           FALSE         FALSE
## 102          DUP16 revm           FALSE         FALSE
## 103          SWAP1 revm           FALSE         FALSE
## 104          SWAP2 revm           FALSE         FALSE
## 105          SWAP3 revm           FALSE         FALSE
## 106          SWAP4 revm           FALSE         FALSE
## 107          SWAP5 revm           FALSE         FALSE
## 108          SWAP6 revm           FALSE         FALSE
## 109          SWAP7 revm           FALSE         FALSE
## 110          SWAP8 revm           FALSE         FALSE
## 111          SWAP9 revm           FALSE         FALSE
## 112         SWAP10 revm           FALSE         FALSE
## 113         SWAP11 revm           FALSE         FALSE
## 114         SWAP12 revm           FALSE         FALSE
## 115         SWAP13 revm           FALSE         FALSE
## 116         SWAP14 revm           FALSE         FALSE
## 117         SWAP15 revm           FALSE         FALSE
## 118         SWAP16 revm           FALSE         FALSE
##                estimate_marginal_ns arg0_ns          arg1_ns
## 1                  2.86163317701086    <NA>             <NA>
## 2                  3.58987821341003    <NA>             <NA>
## 3                  2.76552998426964    <NA>             <NA>
## 4                  12.1071488054329    <NA>             <NA>
## 5                  14.5387461452149    <NA>             <NA>
## 6                  13.1649977135391    <NA>             <NA>
## 7                  15.9661923910559    <NA>             <NA>
## 8                      9.6851580674    <NA>             <NA>
## 9                  27.8311039625004    <NA>             <NA>
## 10                 18.3717230349195    <NA> 35.1492429667228
## 11                 2.40237813313727    <NA>             <NA>
## 12                 2.46813753030766    <NA>             <NA>
## 13                 2.40671405956645    <NA>             <NA>
## 14                 3.85641205572707    <NA>             <NA>
## 15                 3.78641941709235    <NA>             <NA>
## 16                 2.53432498011178    <NA>             <NA>
## 17                 2.17168257851522    <NA>             <NA>
## 18                  2.4330897059555    <NA>             <NA>
## 19                 2.44250211657751    <NA>             <NA>
## 20                 2.34181346131666    <NA>             <NA>
## 21                 2.19980117951678    <NA>             <NA>
## 22                 2.36240275833916    <NA>             <NA>
## 23                 2.80949019349331    <NA>             <NA>
## 24                 3.53412645512575    <NA>             <NA>
## 25                  3.2118610114136    <NA>             <NA>
## 26                 5.84273039496274    <NA>             <NA>
## 27                 5.76031251146242    <NA>             <NA>
## 28                 6.71094580807455    <NA>             <NA>
## 29                 2.73771428571428    <NA>             <NA>
## 30                 3.09927533563248    <NA>             <NA>
## 31                 2.37563609803992    <NA>             <NA>
## 32                 7.12318934827811    <NA>             <NA>
## 33                 2.39289120780556    <NA>             <NA>
## 34                 8.79580997778795    <NA>             <NA>
## 35                 2.73055154505057    <NA>             <NA>
## 36                 2.28318176726734    <NA>             <NA>
## 37  -0.0000000000000039107328336896    <NA>             <NA>
## 38                 6.05527926878008    <NA>             <NA>
## 39                 2.70607210254933    <NA>             <NA>
## 40                 2.65457329209666    <NA>             <NA>
## 41                 2.88080492377114    <NA>             <NA>
## 42                 2.66344790627944    <NA>             <NA>
## 43                 2.67219520384448    <NA>             <NA>
## 44                 2.24529175791854    <NA>             <NA>
## 45                 1.97967018577624    <NA>             <NA>
## 46                 3.45545502333159    <NA>             <NA>
## 47                 3.89303794182304    <NA>             <NA>
## 48                 2.86099328640939    <NA>             <NA>
## 49                  2.8157235845098    <NA>             <NA>
## 50                  3.9535973155056    <NA>             <NA>
## 51                 2.62288862923474    <NA>             <NA>
## 52                 2.46602265008374    <NA>             <NA>
## 53                 2.28677686088942    <NA>             <NA>
## 54              0.00112652652285716    <NA>             <NA>
## 55                 2.29193320299238    <NA>             <NA>
## 56                 2.31462003072586    <NA>             <NA>
## 57                 2.36523609245517    <NA>             <NA>
## 58                 2.33715838026169    <NA>             <NA>
## 59                 2.36892872123834    <NA>             <NA>
## 60                 2.40470461955972    <NA>             <NA>
## 61                 2.46296598947117    <NA>             <NA>
## 62                 2.41608101695761    <NA>             <NA>
## 63                  2.4059829766135    <NA>             <NA>
## 64                 2.35969794353244    <NA>             <NA>
## 65                 2.43203863949888    <NA>             <NA>
## 66                 2.40238057767567    <NA>             <NA>
## 67                 2.41308596859146    <NA>             <NA>
## 68                 2.43925061078901    <NA>             <NA>
## 69                 2.63893444466638    <NA>             <NA>
## 70                 2.34521295195471    <NA>             <NA>
## 71                 2.50346441545812    <NA>             <NA>
## 72                 2.51121436286199    <NA>             <NA>
## 73                 2.65931077952622    <NA>             <NA>
## 74                 2.48060745405083    <NA>             <NA>
## 75                 2.67452259653787    <NA>             <NA>
## 76                 2.68575969198041    <NA>             <NA>
## 77                 2.69462962962962    <NA>             <NA>
## 78                 2.50964826022458    <NA>             <NA>
## 79                 2.58015936986664    <NA>             <NA>
## 80                 2.68648148148149    <NA>             <NA>
## 81                 2.72191348891316    <NA>             <NA>
## 82                 2.65891146375037    <NA>             <NA>
## 83                 2.71426966865779    <NA>             <NA>
## 84                 2.69980222941389    <NA>             <NA>
## 85                 2.76422865057575    <NA>             <NA>
## 86                 2.66802515559227    <NA>             <NA>
## 87                 2.36472716219839    <NA>             <NA>
## 88                 2.34106630495626    <NA>             <NA>
## 89                 2.31244596531295    <NA>             <NA>
## 90                 2.28827873759052    <NA>             <NA>
## 91                 2.31358824483481    <NA>             <NA>
## 92                 2.31636057073772    <NA>             <NA>
## 93                 2.25838230866824    <NA>             <NA>
## 94                 2.24547580146202    <NA>             <NA>
## 95                 2.26252627743828    <NA>             <NA>
## 96                 2.30630259439683    <NA>             <NA>
## 97                 2.33067404532399    <NA>             <NA>
## 98                 2.28275281553476    <NA>             <NA>
## 99                 2.36085385122489    <NA>             <NA>
## 100                2.25601566766521    <NA>             <NA>
## 101                 2.3410043980739    <NA>             <NA>
## 102                  2.368539503121    <NA>             <NA>
## 103                2.61715475534659    <NA>             <NA>
## 104                2.58929330295013    <NA>             <NA>
## 105                 2.5090588100305    <NA>             <NA>
## 106                2.61148599922059    <NA>             <NA>
## 107                 2.7336127596368    <NA>             <NA>
## 108                2.76478553261767    <NA>             <NA>
## 109                2.58162811666002    <NA>             <NA>
## 110                2.68915541512192    <NA>             <NA>
## 111                2.72537356359357    <NA>             <NA>
## 112                2.78180244884668    <NA>             <NA>
## 113                2.91598733585316    <NA>             <NA>
## 114                2.81737247252645    <NA>             <NA>
## 115                2.57316836412952    <NA>             <NA>
## 116                2.76861761357033    <NA>             <NA>
## 117                2.50342793266828    <NA>             <NA>
## 118                2.45882401450633    <NA>             <NA>
##                                 arg2_ns     expensive_ns arg0_ns_stderr
## 1                                  <NA>             <NA>           <NA>
## 2                                  <NA>             <NA>           <NA>
## 3                                  <NA>             <NA>           <NA>
## 4                                  <NA>  7.4411977916547           <NA>
## 5                                  <NA> 7.95304115084793           <NA>
## 6                                  <NA>  6.6225636265391           <NA>
## 7                                  <NA>    5.58011146072           <NA>
## 8                                  <NA> 21.9140019413379           <NA>
## 9                                  <NA> 16.4386842908286           <NA>
## 10                                 <NA>             <NA>           <NA>
## 11                                 <NA>             <NA>           <NA>
## 12                                 <NA>             <NA>           <NA>
## 13                                 <NA>             <NA>           <NA>
## 14                                 <NA>             <NA>           <NA>
## 15                                 <NA>             <NA>           <NA>
## 16                                 <NA>             <NA>           <NA>
## 17                                 <NA>             <NA>           <NA>
## 18                                 <NA>             <NA>           <NA>
## 19                                 <NA>             <NA>           <NA>
## 20                                 <NA>             <NA>           <NA>
## 21                                 <NA>             <NA>           <NA>
## 22                                 <NA>             <NA>           <NA>
## 23                                 <NA>             <NA>           <NA>
## 24                                 <NA>             <NA>           <NA>
## 25                                 <NA>             <NA>           <NA>
## 26                                 <NA>             <NA>           <NA>
## 27                                 <NA>             <NA>           <NA>
## 28                                 <NA>             <NA>           <NA>
## 29                                 <NA>             <NA>           <NA>
## 30                                 <NA>             <NA>           <NA>
## 31                                 <NA>             <NA>           <NA>
## 32                  0.00324673455466849             <NA>           <NA>
## 33                                 <NA>             <NA>           <NA>
## 34                  0.00310790946080824             <NA>           <NA>
## 35                                 <NA>             <NA>           <NA>
## 36                                 <NA>             <NA>           <NA>
## 37  0.000000000000000000343808262507671             <NA>           <NA>
## 38                                 <NA>             <NA>           <NA>
## 39                                 <NA>             <NA>           <NA>
## 40                                 <NA>             <NA>           <NA>
## 41                                 <NA>             <NA>           <NA>
## 42                                 <NA>             <NA>           <NA>
## 43                                 <NA>             <NA>           <NA>
## 44                                 <NA>             <NA>           <NA>
## 45                                 <NA>             <NA>           <NA>
## 46                                 <NA>             <NA>           <NA>
## 47                                 <NA>             <NA>           <NA>
## 48                                 <NA>             <NA>           <NA>
## 49                                 <NA>             <NA>           <NA>
## 50                                 <NA>             <NA>           <NA>
## 51                                 <NA>             <NA>           <NA>
## 52                                 <NA>             <NA>           <NA>
## 53                                 <NA>             <NA>           <NA>
## 54                                 <NA>             <NA>           <NA>
## 55                                 <NA>             <NA>           <NA>
## 56                                 <NA>             <NA>           <NA>
## 57                                 <NA>             <NA>           <NA>
## 58                                 <NA>             <NA>           <NA>
## 59                                 <NA>             <NA>           <NA>
## 60                                 <NA>             <NA>           <NA>
## 61                                 <NA>             <NA>           <NA>
## 62                                 <NA>             <NA>           <NA>
## 63                                 <NA>             <NA>           <NA>
## 64                                 <NA>             <NA>           <NA>
## 65                                 <NA>             <NA>           <NA>
## 66                                 <NA>             <NA>           <NA>
## 67                                 <NA>             <NA>           <NA>
## 68                                 <NA>             <NA>           <NA>
## 69                                 <NA>             <NA>           <NA>
## 70                                 <NA>             <NA>           <NA>
## 71                                 <NA>             <NA>           <NA>
## 72                                 <NA>             <NA>           <NA>
## 73                                 <NA>             <NA>           <NA>
## 74                                 <NA>             <NA>           <NA>
## 75                                 <NA>             <NA>           <NA>
## 76                                 <NA>             <NA>           <NA>
## 77                                 <NA>             <NA>           <NA>
## 78                                 <NA>             <NA>           <NA>
## 79                                 <NA>             <NA>           <NA>
## 80                                 <NA>             <NA>           <NA>
## 81                                 <NA>             <NA>           <NA>
## 82                                 <NA>             <NA>           <NA>
## 83                                 <NA>             <NA>           <NA>
## 84                                 <NA>             <NA>           <NA>
## 85                                 <NA>             <NA>           <NA>
## 86                                 <NA>             <NA>           <NA>
## 87                                 <NA>             <NA>           <NA>
## 88                                 <NA>             <NA>           <NA>
## 89                                 <NA>             <NA>           <NA>
## 90                                 <NA>             <NA>           <NA>
## 91                                 <NA>             <NA>           <NA>
## 92                                 <NA>             <NA>           <NA>
## 93                                 <NA>             <NA>           <NA>
## 94                                 <NA>             <NA>           <NA>
## 95                                 <NA>             <NA>           <NA>
## 96                                 <NA>             <NA>           <NA>
## 97                                 <NA>             <NA>           <NA>
## 98                                 <NA>             <NA>           <NA>
## 99                                 <NA>             <NA>           <NA>
## 100                                <NA>             <NA>           <NA>
## 101                                <NA>             <NA>           <NA>
## 102                                <NA>             <NA>           <NA>
## 103                                <NA>             <NA>           <NA>
## 104                                <NA>             <NA>           <NA>
## 105                                <NA>             <NA>           <NA>
## 106                                <NA>             <NA>           <NA>
## 107                                <NA>             <NA>           <NA>
## 108                                <NA>             <NA>           <NA>
## 109                                <NA>             <NA>           <NA>
## 110                                <NA>             <NA>           <NA>
## 111                                <NA>             <NA>           <NA>
## 112                                <NA>             <NA>           <NA>
## 113                                <NA>             <NA>           <NA>
## 114                                <NA>             <NA>           <NA>
## 115                                <NA>             <NA>           <NA>
## 116                                <NA>             <NA>           <NA>
## 117                                <NA>             <NA>           <NA>
## 118                                <NA>             <NA>           <NA>
##         arg1_ns_stderr                      arg2_ns_stderr expensive_ns_stderr
## 1                 <NA>                                <NA>                <NA>
## 2                 <NA>                                <NA>                <NA>
## 3                 <NA>                                <NA>                <NA>
## 4                 <NA>                                <NA>   0.467750681306078
## 5                 <NA>                                <NA>   0.459707488816463
## 6                 <NA>                                <NA>   0.497526258156315
## 7                 <NA>                                <NA>   0.460408581607353
## 8                 <NA>                                <NA>    1.12096044914081
## 9                 <NA>                                <NA>    1.22993212887081
## 10  0.0940282533542338                                <NA>                <NA>
## 11                <NA>                                <NA>                <NA>
## 12                <NA>                                <NA>                <NA>
## 13                <NA>                                <NA>                <NA>
## 14                <NA>                                <NA>                <NA>
## 15                <NA>                                <NA>                <NA>
## 16                <NA>                                <NA>                <NA>
## 17                <NA>                                <NA>                <NA>
## 18                <NA>                                <NA>                <NA>
## 19                <NA>                                <NA>                <NA>
## 20                <NA>                                <NA>                <NA>
## 21                <NA>                                <NA>                <NA>
## 22                <NA>                                <NA>                <NA>
## 23                <NA>                                <NA>                <NA>
## 24                <NA>                                <NA>                <NA>
## 25                <NA>                                <NA>                <NA>
## 26                <NA>                                <NA>                <NA>
## 27                <NA>                                <NA>                <NA>
## 28                <NA>                                <NA>                <NA>
## 29                <NA>                                <NA>                <NA>
## 30                <NA>                                <NA>                <NA>
## 31                <NA>                                <NA>                <NA>
## 32                <NA>               0.0000343926656372466                <NA>
## 33                <NA>                                <NA>                <NA>
## 34                <NA>               0.0000386939681244167                <NA>
## 35                <NA>                                <NA>                <NA>
## 36                <NA>                                <NA>                <NA>
## 37                <NA> 0.000000000000000000195185779628588                <NA>
## 38                <NA>                                <NA>                <NA>
## 39                <NA>                                <NA>                <NA>
## 40                <NA>                                <NA>                <NA>
## 41                <NA>                                <NA>                <NA>
## 42                <NA>                                <NA>                <NA>
## 43                <NA>                                <NA>                <NA>
## 44                <NA>                                <NA>                <NA>
## 45                <NA>                                <NA>                <NA>
## 46                <NA>                                <NA>                <NA>
## 47                <NA>                                <NA>                <NA>
## 48                <NA>                                <NA>                <NA>
## 49                <NA>                                <NA>                <NA>
## 50                <NA>                                <NA>                <NA>
## 51                <NA>                                <NA>                <NA>
## 52                <NA>                                <NA>                <NA>
## 53                <NA>                                <NA>                <NA>
## 54                <NA>                                <NA>                <NA>
## 55                <NA>                                <NA>                <NA>
## 56                <NA>                                <NA>                <NA>
## 57                <NA>                                <NA>                <NA>
## 58                <NA>                                <NA>                <NA>
## 59                <NA>                                <NA>                <NA>
## 60                <NA>                                <NA>                <NA>
## 61                <NA>                                <NA>                <NA>
## 62                <NA>                                <NA>                <NA>
## 63                <NA>                                <NA>                <NA>
## 64                <NA>                                <NA>                <NA>
## 65                <NA>                                <NA>                <NA>
## 66                <NA>                                <NA>                <NA>
## 67                <NA>                                <NA>                <NA>
## 68                <NA>                                <NA>                <NA>
## 69                <NA>                                <NA>                <NA>
## 70                <NA>                                <NA>                <NA>
## 71                <NA>                                <NA>                <NA>
## 72                <NA>                                <NA>                <NA>
## 73                <NA>                                <NA>                <NA>
## 74                <NA>                                <NA>                <NA>
## 75                <NA>                                <NA>                <NA>
## 76                <NA>                                <NA>                <NA>
## 77                <NA>                                <NA>                <NA>
## 78                <NA>                                <NA>                <NA>
## 79                <NA>                                <NA>                <NA>
## 80                <NA>                                <NA>                <NA>
## 81                <NA>                                <NA>                <NA>
## 82                <NA>                                <NA>                <NA>
## 83                <NA>                                <NA>                <NA>
## 84                <NA>                                <NA>                <NA>
## 85                <NA>                                <NA>                <NA>
## 86                <NA>                                <NA>                <NA>
## 87                <NA>                                <NA>                <NA>
## 88                <NA>                                <NA>                <NA>
## 89                <NA>                                <NA>                <NA>
## 90                <NA>                                <NA>                <NA>
## 91                <NA>                                <NA>                <NA>
## 92                <NA>                                <NA>                <NA>
## 93                <NA>                                <NA>                <NA>
## 94                <NA>                                <NA>                <NA>
## 95                <NA>                                <NA>                <NA>
## 96                <NA>                                <NA>                <NA>
## 97                <NA>                                <NA>                <NA>
## 98                <NA>                                <NA>                <NA>
## 99                <NA>                                <NA>                <NA>
## 100               <NA>                                <NA>                <NA>
## 101               <NA>                                <NA>                <NA>
## 102               <NA>                                <NA>                <NA>
## 103               <NA>                                <NA>                <NA>
## 104               <NA>                                <NA>                <NA>
## 105               <NA>                                <NA>                <NA>
## 106               <NA>                                <NA>                <NA>
## 107               <NA>                                <NA>                <NA>
## 108               <NA>                                <NA>                <NA>
## 109               <NA>                                <NA>                <NA>
## 110               <NA>                                <NA>                <NA>
## 111               <NA>                                <NA>                <NA>
## 112               <NA>                                <NA>                <NA>
## 113               <NA>                                <NA>                <NA>
## 114               <NA>                                <NA>                <NA>
## 115               <NA>                                <NA>                <NA>
## 116               <NA>                                <NA>                <NA>
## 117               <NA>                                <NA>                <NA>
## 118               <NA>                                <NA>                <NA>
write.csv(estimates, paste0("../../local/", env, "_argument_estimated_cost.csv"), quote=FALSE, row.names=FALSE)